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Microsoft ajoute des primitives d'apprentissage automatique directement à HLSL et un second chemin pour exécuter des graphes ML plus volumineux au sein de l'écosystème DirectX. Cela change l'endroit où le rendu neuronal peut vivre dans les futurs jeux PC.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--info my-6 rounded-xl border p-5 border-blue-300 bg-blue-50 dark:border-blue-900 dark:bg-blue-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Réponse directe\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>DirectX devient une plateforme graphique capable de ML.\u003C\u002Fstrong> Les petites charges de travail neuronales peuvent s'exécuter directement dans les shaders via DX Linear Algebra, tandis que les modèles plus volumineux sont ciblés par le DirectX Compute Graph Compiler. L'objectif est de permettre aux moteurs de jeu d'utiliser le matériel IA du GPU sans construire un chemin spécifique à chaque fournisseur pour chaque technique.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">Statut actuel\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">En septembre 2026, \u003Cstrong>DX Linear Algebra est encore une technologie en préversion\u003C\u002Fstrong> dans le chemin de préversion Shader Model 6.10 \u002F Agility SDK. Microsoft a annoncé le DirectX Compute Graph Compiler en préversion privée plutôt que comme une fonctionnalité de vente au détail largement disponible. Cet article explique l'architecture et l'orientation, et non l'affirmation que chaque jeu actuel peut l'utiliser aujourd'hui.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Cnav class=\"editorjs-toc\" data-editorjs-toc=\"true\" aria-label=\"Contenu\">\u003Cstrong class=\"editorjs-toc__title\">Contenu\u003C\u002Fstrong>\u003Col class=\"editorjs-toc__list editorjs-toc__list--depth-0\">\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-5\" class=\"editorjs-toc__link\">Pourquoi le rendu neuronal a besoin de nouvelles primitives DirectX\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-9\" class=\"editorjs-toc__link\">Le modèle ML DirectX à deux niveaux\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-12\" class=\"editorjs-toc__link\">Ce que DX Linear Algebra apporte réellement à un shader\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-17\" class=\"editorjs-toc__link\">Pourquoi Cooperative Vector n'était que le début\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-21\" class=\"editorjs-toc__link\">Que peut-on réellement exécuter dans un shader ?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-24\" class=\"editorjs-toc__link\">Pourquoi cela importe pour la mémoire de texture\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-29\" class=\"editorjs-toc__link\">Pourquoi les modèles complets nécessitent un chemin différent\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-33\" class=\"editorjs-toc__link\">La frontière shader-ou-graphe\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-35\" class=\"editorjs-toc__link\">Pourquoi le support multi-fournisseurs importe plus qu'une autre fonctionnalité d'IA\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-40\" class=\"editorjs-toc__link\">Quel matériel prend en charge l'aperçu actuel ?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-44\" class=\"editorjs-toc__link\">Le rendu neuronal devient une infrastructure\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-49\" class=\"editorjs-toc__link\">La pile de rendu neuronal devient stratifiée\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-51\" class=\"editorjs-toc__link\">Pourquoi le profilage unifié est important\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-55\" class=\"editorjs-toc__link\">Cela ne signifie pas que chaque shader deviendra un réseau de neurones\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-59\" class=\"editorjs-toc__link\">Le test de valeur du shader neuronal\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-61\" class=\"editorjs-toc__link\">Ce que cela signifie pour les joueurs\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-65\" class=\"editorjs-toc__link\">Qu'est-ce qui changerait cette réponse ?\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-69\" class=\"editorjs-toc__link\">Limitations\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-72\" class=\"editorjs-toc__link\">Conclusion\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-76\" class=\"editorjs-toc__link\">FAQ\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-78\" class=\"editorjs-toc__link\">Glossaire\u003C\u002Fa>\u003C\u002Fli>\u003Cli class=\"editorjs-toc__item\">\u003Ca href=\"#section-80\" class=\"editorjs-toc__link\">Sources principales\u003C\u002Fa>\u003C\u002Fli>\u003C\u002Fol>\u003C\u002Fnav>\n\u003Ch2 id=\"section-5\">Pourquoi le rendu neuronal a besoin de nouvelles primitives DirectX\u003C\u002Fh2>\n\u003Cp>Les GPU modernes contiennent déjà du matériel spécialisé pour les opérations matricielles utilisées par l'apprentissage automatique. Le problème pour un développeur de jeu n'est pas seulement de savoir si ce matériel existe, mais comment y accéder efficacement depuis un pipeline graphique en temps réel.\u003C\u002Fp>\n\u003Cp>Microsoft a d'abord exploré cela avec le support Cooperative Vector. En 2026, ce travail a évolué vers une conception DirectX Linear Algebra plus large qui prend en charge les opérations vecteur-matrice et matrice-matrice.\u003C\u002Fp>\n\u003Cp>Cela compte parce que différentes tâches de rendu neuronal ont différentes formes. Un petit modèle évaluant le comportement des matériaux par pixel n'est pas la même charge de travail qu'un grand graphe de super-résolution ou de débruitage.\u003C\u002Fp>\n\u003Ch2 id=\"section-9\">Le modèle ML DirectX à deux niveaux\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Deux façons dont le ML peut entrer dans le pipeline graphique DirectX\u003C\u002Fh3>\u003Cdiv class=\"flex flex-col sm:flex-row gap-3\">\u003Cdiv class=\"editorjs-process__step min-w-0 flex-1 rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">1. ML au niveau du shader\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">De petites charges de travail neuronales ou d'algèbre linéaire s'exécutent directement depuis HLSL aux côtés du code de shader traditionnel.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90 sm:rotate-0\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0 flex-1 rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. DX Linear Algebra\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Le shader peut demander des opérations vectorielles et matricielles accélérées par le matériel au lieu d'implémenter manuellement chaque primitive ML.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90 sm:rotate-0\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0 flex-1 rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">3. ML au niveau du modèle\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Les réseaux neuronaux plus volumineux sont représentés comme des graphes de calcul complets plutôt que comme des fragments de shader écrits à la main.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__arrow shrink-0 self-center text-xl text-gray-400 rotate-90 sm:rotate-0\" aria-hidden=\"true\">→\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0 flex-1 rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. DirectX Compute Graph Compiler\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Le chemin de compilation prévu par Microsoft analyse et abaisse ces graphes en charges de travail GPU optimisées intégrées aux files d'attente et listes de commandes D3D12.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Caside class=\"editorjs-callout editorjs-callout--success my-6 rounded-xl border p-5 border-emerald-300 bg-emerald-50 dark:border-emerald-900 dark:bg-emerald-950\u002F20\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">La différence simple\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">\u003Cstrong>DX Linear Algebra :\u003C\u002Fstrong> placer de petits calculs ML dans le shader.\u003Cbr>\u003Cstrong>Compute Graph Compiler :\u003C\u002Fstrong> apporter un modèle ML plus volumineux dans le moteur sous forme de graphe.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-12\">Ce que DX Linear Algebra apporte réellement à un shader\u003C\u002Fh2>\n\u003Cp>Le HLSL traditionnel est construit autour d'opérations graphiques et de calcul. Les charges de travail neuronales reposent fortement sur l'algèbre linéaire : vecteurs, matrices, multiplication, accumulation et dispositions de données optimisées pour ces opérations.\u003C\u002Fp>\n\u003Cp>La préversion de Shader Model 6.10 ajoute des API matricielles de première classe afin que les développeurs puissent exprimer ces charges de travail plus directement et laisser le pilote les mapper sur du matériel spécialisé.\u003C\u002Fp>\n\u003Cp>La préversion d'avril 2026 de Microsoft décrit explicitement cela comme un chemin unifié pour le rendu neuronal, le ML et les charges de travail de traitement d'image plutôt que comme une fonctionnalité purement graphique.\u003C\u002Fp>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Mathématiques de shader traditionnelles vs mathématiques de shader orientées ML\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Focus traditionnel du shader\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Focus orienté ML\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Travail typique\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Chemin matériel\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Expression du développeur\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-17\">Pourquoi Cooperative Vector n'était que le début\u003C\u002Fh2>\n\u003Cp>Cooperative Vector permettait aux threads de shader de demander un travail vecteur-matrice que les pilotes pouvaient mapper sur du matériel spécialisé. C'était utile pour les charges de travail hautement parallèles par pixel.\u003C\u002Fp>\n\u003Cp>Microsoft a ensuite conclu que de nombreuses charges de travail importantes de ML nécessitent plus que des opérations vecteur-matrice. La super-résolution, le débruitage, la reconstruction temporelle, les modèles d'image plus grands et l'inférence générale peuvent nécessiter des opérations matrice-matrice et un travail partagé entre de nombreux threads.\u003C\u002Fp>\n\u003Cp>DX Linear Algebra élargit donc le modèle au lieu de traiter Cooperative Vector comme l'abstraction finale.\u003C\u002Fp>\n\u003Ch2 id=\"section-21\">Que peut-on réellement exécuter dans un shader ?\u003C\u002Fh2>\n\u003Cp>Les charges de travail ML au niveau du shader les plus intéressantes sont suffisamment petites pour s'exécuter à proximité des données graphiques sur lesquelles elles opèrent.\u003C\u002Fp>\n\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Charge de travail\u003C\u002Fth>\u003Cth class=\"border border-gray-300 px-4 py-2 text-left font-semibold\">Pourquoi le ML au niveau du shader convient\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Compression de texture neuronale\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Un petit réseau peut reconstruire les informations de texture près du point où le shader en a besoin\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Évaluation de matériau neuronal\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Une fonction apprise peut remplacer ou augmenter des calculs de matériau coûteux écrits à la main\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Mise en cache de radiance neuronale\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">L'inférence par pixel ou locale peut estimer les informations d'éclairage à partir du comportement appris de la scène\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Petits noyaux de débruitage\u002Freconstruction\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Les opérations ML peuvent se situer directement à côté de l'étape de rendu qu'elles améliorent\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Inférence de traitement d'image\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-2\">Les opérations matricielles peuvent être intégrées au traitement GPU sans runtime externe séparé\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\n\u003Ch2 id=\"section-24\">Pourquoi cela importe pour la mémoire de texture\u003C\u002Fh2>\n\u003Cp>L'un des exemples récurrents de Microsoft est la compression de texture neuronale.\u003C\u002Fp>\n\u003Cp>Au lieu de stocker chaque canal de texture à une fidélité conventionnelle, un jeu peut stocker une représentation plus compacte et utiliser un petit réseau neuronal pour reconstruire les détails pendant le rendu.\u003C\u002Fp>\n\u003Cp>Cela échange un certain travail d'inférence GPU contre une pression de stockage ou de mémoire réduite. Le bénéfice exact dépend de la technique et du matériel, mais le changement architectural est important : certains détails visuels peuvent devenir du calcul plutôt que des données stockées.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">L'utilisation de la VRAM n'est pas une exigence de VRAM : pourquoi un compteur de mémoire plein ne raconte pas toute l'histoire\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Un guide pratique sur la capacité de VRAM, les budgets de résidence, les ensembles de travail et pourquoi la pression mémoire est plus compliquée qu'un seul chiffre d'utilisation.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Lire le guide VRAM →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-29\">Pourquoi les modèles complets nécessitent un chemin différent\u003C\u002Fh2>\n\u003Cp>Écrire à la main quelques opérations matricielles en HLSL est pratique pour de petites fonctions neuronales. Cela devient beaucoup moins pratique lorsque la charge de travail est un modèle moderne complet avec de nombreuses couches, dépendances et tenseurs intermédiaires.\u003C\u002Fp>\n\u003Cp>Le DirectX Compute Graph Compiler de Microsoft est conçu pour cette classe plus large de charges de travail.\u003C\u002Fp>\n\u003Cp>Au lieu de réécrire le modèle en code de shader personnalisé, le compilateur peut accepter un graphe de calcul, analyser l'ensemble du graphe, planifier la mémoire, fusionner les opérations et abaisser le résultat en travail GPU qui s'intègre à DirectX 12.\u003C\u002Fp>\n\u003Ch2 id=\"section-33\">La frontière shader-ou-graphe\u003C\u002Fh2>\n\u003Csection class=\"editorjs-comparison my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Quand la charge de travail ML appartient au HLSL vs un compilateur de modèle\u003C\u002Fh3>\u003Cdiv class=\"overflow-x-auto\">\u003Ctable class=\"w-full border-collapse\">\u003Cthead>\u003Ctr>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left dark:border-gray-700 dark:bg-gray-900\">\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Chemin au niveau du shader\u003C\u002Fth>\u003Cth class=\"border border-gray-300 bg-gray-50 px-4 py-3 text-left font-semibold dark:border-gray-700 dark:bg-gray-900\">Chemin au niveau du modèle\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Taille du modèle\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Style d'exécution\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Écriture\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Portée de l'optimisation\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Cth class=\"border border-gray-300 px-4 py-3 text-left font-semibold dark:border-gray-700\">Utilisation typique\u003C\u002Fth>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003Ctd class=\"border border-gray-300 px-4 py-3 dark:border-gray-700\">\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-35\">Pourquoi le support multi-fournisseurs importe plus qu'une autre fonctionnalité d'IA\u003C\u002Fh2>\n\u003Cp>AMD, Intel, NVIDIA et Qualcomm exposent différentes architectures GPU et différentes formes d'accélération matricielle dédiée.\u003C\u002Fp>\n\u003Cp>Une abstraction DirectX donne à Microsoft et aux fournisseurs de pilotes un endroit pour traduire le HLSL courant ou le ML au niveau du graphe vers le bon chemin matériel.\u003C\u002Fp>\n\u003Cp>Cela ne rend pas tous les GPU également rapides, mais cela peut réduire le besoin pour un moteur de jeu d'implémenter une API de rendu neuronal complètement différente pour chaque fournisseur.\u003C\u002Fp>\n\u003Caside class=\"editorjs-callout editorjs-callout--note my-6 rounded-xl border p-5 border-gray-300 bg-gray-50 dark:border-gray-700 dark:bg-gray-900\u002F40\" role=\"note\">\u003Cstrong class=\"block mb-2 text-gray-900 dark:text-gray-100\">La portabilité est la véritable histoire de plateforme\u003C\u002Fstrong>\u003Cdiv class=\"text-gray-700 dark:text-gray-200\">La partie intéressante n'est pas que DirectX puisse « exécuter de l'IA ». Les GPU le pouvaient déjà. Le changement de plateforme est que \u003Cstrong>le ML devient exprimable via une API graphique et une chaîne d'outils communes\u003C\u002Fstrong>.\u003C\u002Fdiv>\u003C\u002Faside>\n\u003Ch2 id=\"section-40\">Quel matériel prend en charge l'aperçu actuel ?\u003C\u002Fh2>\n\u003Cp>La réponse dépend de l'opération d'algèbre linéaire spécifique et du pilote d'aperçu.\u003C\u002Fp>\n\u003Cp>Le tableau de prise en charge de l'aperçu du SDK Agility 1.721 de Microsoft répertorie LinAlg VectorAccumulate pour le matériel AMD Radeon RX 9000 Series, le matériel Intel Xe2 ou plus récent via un pilote à venir, et le matériel NVIDIA RTX via le chemin d'aperçu pris en charge.\u003C\u002Fp>\n\u003Cp>Il s'agit d'une prise en charge à l'ère de l'aperçu, pas d'une garantie de vente au détail universelle. La prise en charge du matériel et des pilotes peut changer avant la finalisation.\u003C\u002Fp>\n\u003Ch2 id=\"section-44\">Le rendu neuronal devient une infrastructure\u003C\u002Fh2>\n\u003Cp>DLSS, FSR et d'autres technologies graphiques neuronales sont souvent présentées comme des fonctionnalités de marque visibles dans le menu des paramètres d'un jeu.\u003C\u002Fp>\n\u003Cp>L'algèbre linéaire DirectX indique un changement plus profond. L'opération neuronale peut devenir un détail d'implémentation interne au sein du moteur de rendu plutôt qu'une simple fonctionnalité de post-traitement optionnelle.\u003C\u002Fp>\n\u003Cp>Un développeur pourrait utiliser le ML pour les textures, les matériaux, l'éclairage, la reconstruction ou d'autres fonctions locales sans exposer chacune d'elles comme une option d'IA destinée au consommateur.\u003C\u002Fp>\n\u003Caside class=\"editorjs-referral my-6\">\u003Ca href=\"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes\" class=\"flex flex-col sm:flex-row gap-4 rounded-xl border border-gray-200 dark:border-gray-700 p-4 transition hover:border-primary-500\">\u003Cdiv class=\"min-w-0 flex-1\">\u003Cstrong class=\"block text-lg text-gray-900 dark:text-gray-100\">DLSS 5 n'est pas qu'une mise à l'échelle : ce que le rendu neuronal guidé par la 3D change réellement\u003C\u002Fstrong>\u003Cp class=\"mt-2 text-sm text-gray-600 dark:text-gray-300\">Comment le rendu neuronal dépasse la mise à l'échelle et les images générées pour s'étendre à la reconstruction des matériaux et de l'éclairage au sein du pipeline graphique.\u003C\u002Fp>\u003Cspan class=\"mt-3 inline-flex text-sm font-medium text-primary-600 dark:text-primary-400\">Lire le guide du rendu neuronal DLSS 5 →\u003C\u002Fspan>\u003C\u002Fdiv>\u003C\u002Fa>\u003C\u002Faside>\n\u003Ch2 id=\"section-49\">La pile de rendu neuronal devient stratifiée\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Un futur pipeline de jeu DirectX possible\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Travail traditionnel du moteur\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">La simulation, la géométrie, la visibilité et le rendu de base restent des responsabilités standard du moteur.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Shaders neuronaux en ligne\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">De petites fonctions apprises reconstruisent les textures, les matériaux ou l'éclairage dans HLSL.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Graphes ML plus grands\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Les modèles complets de reconstruction ou d'inférence s'exécutent via un chemin DirectX au niveau du graphe.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Mappage matériel du fournisseur\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Les pilotes mappent les opérations DirectX courantes sur les unités IA et matricielles spécialisées du GPU.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">Visibilité PIX\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Le travail graphique et le travail ML peuvent être profilés ensemble au lieu de résider dans des runtimes opaques séparés.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-51\">Pourquoi le profilage unifié est important\u003C\u002Fh2>\n\u003Cp>Une charge de travail neuronale qui améliore la qualité de l'image peut quand même nuire à un jeu si elle consomme de manière inattendue du temps de frame, de la bande passante mémoire ou de la VRAM.\u003C\u002Fp>\n\u003Cp>Microsoft inclut explicitement la visibilité PIX unifiée comme partie de l'orientation du Compute Graph Compiler. Cela compte car les développeurs doivent voir le travail graphique et le travail ML dans la même capture de frame.\u003C\u002Fp>\n\u003Cp>Si le rendu neuronal devient une infrastructure, il doit être mesurable comme n'importe quelle autre étape de rendu.\u003C\u002Fp>\n\u003Ch2 id=\"section-55\">Cela ne signifie pas que chaque shader deviendra un réseau de neurones\u003C\u002Fh2>\n\u003Cp>Les mathématiques traditionnelles des shaders restent efficaces, déterministes et faciles à appréhender pour de nombreuses charges de travail.\u003C\u002Fp>\n\u003Cp>Une fonction neuronale a du sens lorsqu'elle peut approximer ou reconstruire quelque chose de coûteux plus efficacement, compresser des données, ou produire une qualité qui nécessiterait autrement trop de calcul ou de mémoire conventionnels.\u003C\u002Fp>\n\u003Cp>L'architecture appropriée restera hybride.\u003C\u002Fp>\n\u003Ch2 id=\"section-59\">Le test de valeur du shader neuronal\u003C\u002Fh2>\n\u003Csection class=\"editorjs-process my-6\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Quand est-ce que l'intégration du ML dans le pipeline de rendu a réellement du sens ?\u003C\u002Fh3>\u003Cdiv class=\"grid grid-cols-1 md:grid-cols-2 xl:grid-cols-3 gap-4\">\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">1\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">1. Identifier l'opération traditionnelle coûteuse\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Quel coût de calcul, de bande passante, de mémoire ou de stockage essayez-vous de remplacer ?\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">2\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">2. Définir le substitut neuronal\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Que peut reconstruire, prédire ou compresser un petit modèle ?\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">3\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">3. Mesurer le coût d'inférence\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Le modèle ML lui-même consomme du temps GPU, de la mémoire et de la bande passante.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">4\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">4. Mesurer la stabilité de la qualité\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Recherchez les artefacts temporels, les erreurs de reconstruction et les cas d'échec.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">5\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">5. Mesurer les économies nettes\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">La technique n'est utile que si le coût traditionnel évité vaut le coût ML ajouté.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv class=\"editorjs-process__step min-w-0  rounded-xl border border-gray-200 dark:border-gray-700 p-4\">\u003Cdiv class=\"text-xs font-semibold text-gray-500 dark:text-gray-400\">6\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 font-semibold text-gray-900 dark:text-gray-100\">6. Tester sur différents fournisseurs\u003C\u002Fdiv>\u003Cdiv class=\"mt-1 text-sm text-gray-600 dark:text-gray-300\">Une abstraction DirectX aide à la portabilité, mais les performances matérielles réelles diffèrent toujours.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-61\">Ce que cela signifie pour les joueurs\u003C\u002Fh2>\n\u003Cp>Les joueurs ne verront peut-être jamais un commutateur « DX Linear Algebra » dans un menu graphique.\u003C\u002Fp>\n\u003Cp>L'impact probable est indirect : des empreintes de textures plus petites, une meilleure reconstruction de l'éclairage, des graphismes neuronaux plus efficaces, ou de nouvelles techniques visuelles qui deviennent pratiques parce que le moteur peut accéder à l'accélération matricielle via un chemin standard.\u003C\u002Fp>\n\u003Cp>La fonctionnalité est plus importante en tant qu'infrastructure qu'en tant que marque.\u003C\u002Fp>\n\u003Ch2 id=\"section-65\">Qu'est-ce qui changerait cette réponse ?\u003C\u002Fh2>\n\u003Cp>La plus grande incertitude réside dans la forme finale de vente au détail de ces API. DX Linear Algebra reste en préversion, et le Compute Graph Compiler n'a pas encore atteint une large disponibilité en version commerciale.\u003C\u002Fp>\n\u003Cp>La prise en charge matérielle finale, les détails de l'API, le comportement du compilateur et l'adoption par les moteurs peuvent changer avant que ces systèmes ne deviennent une infrastructure normale pour les jeux commercialisés.\u003C\u002Fp>\n\u003Cp>Si les principaux moteurs adoptent directement les abstractions, les shaders neuronaux pourraient devenir beaucoup plus courants sans que chaque équipe de jeu implémente chaque technique à partir de zéro.\u003C\u002Fp>\n\u003Ch2 id=\"section-69\">Limitations\u003C\u002Fh2>\n\u003Cp>Cet article explique l'architecture DirectX documentée par Microsoft et les API en préversion. Il ne prétend pas que DX Linear Algebra améliore actuellement les performances dans chaque jeu ni que le Compute Graph Compiler est un produit commercial fini.\u003C\u002Fp>\n\u003Cp>Les exemples de Microsoft décrivent la capacité et l'utilisation prévue. Les avantages réels dépendent du modèle, de l'intégration au moteur, de l'architecture GPU, des pilotes et de la charge de travail.\u003C\u002Fp>\n\u003Ch2 id=\"section-72\">Conclusion\u003C\u002Fh2>\n\u003Cp>Le changement important de DirectX n'est pas une nouvelle case à cocher appelée IA.\u003C\u002Fp>\n\u003Cp>C'est que les mathématiques d'apprentissage automatique intègrent le modèle de programmation graphique lui-même. De petites fonctions neuronales peuvent résider dans les shaders, des modèles plus grands peuvent évoluer vers une compilation au niveau du graphe, et la même chaîne d'outils DirectX peut exposer ces charges de travail sur plusieurs fournisseurs de GPU.\u003C\u002Fp>\n\u003Cp>C'est ainsi que le rendu neuronal cesse d'être une fonctionnalité de marque pour devenir une partie de l'infrastructure de rendu.\u003C\u002Fp>\n\u003Ch2 id=\"section-76\">FAQ\u003C\u002Fh2>\n\u003Csection class=\"editorjs-faq my-6 rounded-xl border border-gray-200 p-5 dark:border-gray-700\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Algèbre linéaire DirectX et shaders neuronaux\u003C\u002Fh3>\u003Cdiv id=\"faq1\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Qu'est-ce que l'algèbre linéaire DirectX ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">C'est un ensemble de fonctionnalités DirectX\u002FHLSL pour les opérations vectorielles et matricielles accélérées par le matériel, utilisées par l'apprentissage automatique, le rendu neuronal et les charges de travail de traitement d'images.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq2\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Qu'est-ce qu'un shader neuronal ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Une description simple et utile est un shader qui inclut une fonction neuronale apprise ou une étape d'inférence ML dans le cadre de son travail graphique.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq3\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">L'algèbre linéaire DX est-elle déjà une fonctionnalité DirectX standard en vente au détail ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">En septembre 2026, elle reste dans la voie de prévisualisation Shader Model 6.10 \u002F Agility SDK.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq4\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Qu'est-ce que le compilateur de graphes de calcul DirectX ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">C'est l'API de compilateur ML au niveau du modèle annoncée par Microsoft, destinée à prendre des graphes de calcul plus grands et à les abaisser en charges de travail GPU optimisées intégrées à D3D12.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq5\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Pourquoi ne pas exécuter chaque modèle ML directement en HLSL ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Les petites fonctions s'adaptent bien à l'écriture au niveau du shader, tandis que les grands modèles bénéficient d'une optimisation globale du graphe, d'une planification de la mémoire et d'une fusion d'opérateurs.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003Cdiv id=\"faq6\" class=\"border-t border-gray-200 py-4 first:border-t-0 dark:border-gray-700\">\u003Ch4 class=\"font-semibold text-gray-900 dark:text-gray-100\">Cela remplacera-t-il DLSS ou FSR ?\u003C\u002Fh4>\u003Cdiv class=\"mt-2 text-gray-600 dark:text-gray-300\">Pas directement. DirectX fournit une infrastructure de bas niveau que les fournisseurs et les développeurs peuvent utiliser pour les graphiques neuronaux. Les technologies de marque peuvent toujours implémenter leurs propres modèles et stratégies d'intégration.\u003C\u002Fdiv>\u003C\u002Fdiv>\u003C\u002Fsection>\n\u003Ch2 id=\"section-78\">Glossaire\u003C\u002Fh2>\n\u003Csection class=\"editorjs-glossary my-6 rounded-xl border border-gray-200 dark:border-gray-700 p-5\">\u003Ch3 class=\"mb-3 text-lg font-semibold\">Termes clés du ML DirectX\u003C\u002Fh3>\u003Cdl>\u003Cdiv id=\"dx-linear-algebra\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Algèbre linéaire DX\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">API DirectX\u002FHLSL pour les opérations vectorielles et matricielles accélérées destinées au rendu neuronal, au ML et aux charges de travail de traitement d'images.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"cooperative-vector\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Vecteur coopératif\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Une approche DirectX antérieure pour les opérations vectorielles-matricielles accélérées dans les shaders qui a contribué à établir la voie du rendu neuronal au niveau du shader.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"shader-model-6-10\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Shader Model 6.10\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">La génération de modèle de shader en prévisualisation contenant les API matricielles actuelles d'algèbre linéaire DirectX.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"compute-graph-compiler\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Compilateur de graphes de calcul DirectX\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">L'API de compilateur annoncée par Microsoft pour optimiser et exécuter des graphes de calcul ML plus grands en tant que charges de travail GPU DirectX natives.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"neural-shader\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Shader neuronal\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un terme pratique pour un shader qui effectue une inférence apprise ou un calcul neuronal dans le cadre du traitement graphique.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"shader-or-graph-boundary\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Frontière shader-ou-graphe\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un modèle Figure Rocks pour décider si une charge de travail ML doit être de petites mathématiques de shader en ligne ou un graphe de calcul plus grand au niveau du modèle.\u003C\u002Fdd>\u003C\u002Fdiv>\u003Cdiv id=\"neural-shader-value-test\" class=\"border-t border-gray-200 dark:border-gray-700 py-3 first:border-t-0\">\u003Cdt class=\"font-semibold text-gray-900 dark:text-gray-100\">Test de valeur du shader neuronal\u003C\u002Fdt>\u003Cdd class=\"mt-1 text-gray-600 dark:text-gray-300\">Un flux de travail Figure Rocks pour décider si le coût de calcul ou de mémoire traditionnel évité par une technique neuronale vaut son coût d'inférence et ses compromis de qualité.\u003C\u002Fdd>\u003C\u002Fdiv>\u003C\u002Fdl>\u003C\u002Fsection>\n\u003Ch2 id=\"section-80\">Sources principales\u003C\u002Fh2>\n\u003Ca href=\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fevolving-directx-for-the-ml-era-on-windows\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Microsoft DirectX — Faire évoluer DirectX pour l'ère du ML sur Windows\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Aperçu officiel de l'architecture GDC 2026 couvrant le ML au niveau du shader, l'algèbre linéaire DX et le compilateur de graphes de calcul DirectX.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fd3d12-linalg-preview\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Microsoft DirectX — Aperçu de la matrice LinAlg D3D12\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Aperçu officiel d'avril 2026 expliquant les API unifiées d'algèbre linéaire, les opérations matricielles et la motivation du rendu neuronal.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fannouncing-agilitysdk-721-preview-and-more-shader-model-6-10-features\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Microsoft DirectX — Aperçu du SDK Agility 1.721\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Version officielle de mai 2026 documentant les mises à jour d'algèbre linéaire de Shader Model 6.10 et la prise en charge matérielle en prévisualisation.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdeveloper.microsoft.com\u002Fen-us\u002Fgames\u002Farticles\u002F2026\u002F03\u002Fgdc-2026-evolving-directx-for-ml-era-on-windows\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Microsoft Game Dev — GDC 2026 : Faire évoluer DirectX pour l'ère du ML\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Résumé officiel de Microsoft Game Dev expliquant le ML au niveau du shader et du modèle et le rôle du compilateur de graphes de calcul.\u003C\u002Fp>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fcooperative-vector\u002F\" target=\"_blank\" rel=\"noopener noreferrer\" class=\"editorjs-link-tool block border border-gray-200 dark:border-gray-700 rounded-lg p-4 transition text-gray-900 dark:text-gray-100 hover:border-primary-500 hover:bg-primary-50 dark:hover:bg-gray-900 hover:text-gray-900 dark:hover:text-gray-100\">\u003Cstrong class=\"block font-semibold\">Microsoft DirectX — Vecteur coopératif D3D12\u003C\u002Fstrong>\u003Cp class=\"text-sm text-gray-600 dark:text-gray-400\">Contexte officiel sur les opérations vectorielles\u002Fmatricielles accélérées par le matériel et le rendu neuronal directement à partir des threads de shader.\u003C\u002Fp>\u003C\u002Fa>",{"time":538,"blocks":539,"version":1178},1790378153771,[540,546,554,561,568,574,579,584,589,594,614,621,626,631,636,641,668,673,678,683,688,693,698,722,727,732,737,742,751,756,761,766,771,776,809,814,819,824,829,835,840,845,850,855,860,865,870,875,883,888,909,914,919,924,929,934,939,944,949,954,978,983,988,993,998,1003,1008,1013,1018,1023,1028,1033,1038,1043,1048,1053,1058,1088,1093,1127,1132,1142,1151,1160,1169],{"id":541,"data":542,"type":544,"tunes":545},"intro",{"text":543},"DirectX n'est plus seulement une API graphique qui envoie des shaders traditionnels au GPU. Microsoft ajoute des primitives d'apprentissage automatique directement à HLSL et un second chemin pour exécuter des graphes ML plus volumineux au sein de l'écosystème DirectX. Cela change l'endroit où le rendu neuronal peut vivre dans les futurs jeux PC.","paragraph",{},{"id":547,"data":548,"type":552,"tunes":553},"direct",{"body":549,"title":550,"variant":551},"\u003Cstrong>DirectX devient une plateforme graphique capable de ML.\u003C\u002Fstrong> Les petites charges de travail neuronales peuvent s'exécuter directement dans les shaders via DX Linear Algebra, tandis que les modèles plus volumineux sont ciblés par le DirectX Compute Graph Compiler. L'objectif est de permettre aux moteurs de jeu d'utiliser le matériel IA du GPU sans construire un chemin spécifique à chaque fournisseur pour chaque technique.","Réponse directe","info","callout",{},{"id":555,"data":556,"type":552,"tunes":560},"status-note",{"body":557,"title":558,"variant":559},"En septembre 2026, \u003Cstrong>DX Linear Algebra est encore une technologie en préversion\u003C\u002Fstrong> dans le chemin de préversion Shader Model 6.10 \u002F Agility SDK. Microsoft a annoncé le DirectX Compute Graph Compiler en préversion privée plutôt que comme une fonctionnalité de vente au détail largement disponible. Cet article explique l'architecture et l'orientation, et non l'affirmation que chaque jeu actuel peut l'utiliser aujourd'hui.","Statut actuel","note",{},{"id":562,"data":563,"type":566,"tunes":567},"toc",{"title":564,"maxLevel":565,"minLevel":47},"Contenu",3,"tableOfContents",{},{"id":569,"data":570,"type":572,"tunes":573},"h-why",{"text":571,"level":47},"Pourquoi le rendu neuronal a besoin de nouvelles primitives DirectX","header",{},{"id":575,"data":576,"type":544,"tunes":578},"p-why-1",{"text":577},"Les GPU modernes contiennent déjà du matériel spécialisé pour les opérations matricielles utilisées par l'apprentissage automatique. Le problème pour un développeur de jeu n'est pas seulement de savoir si ce matériel existe, mais comment y accéder efficacement depuis un pipeline graphique en temps réel.",{},{"id":580,"data":581,"type":544,"tunes":583},"p-why-2",{"text":582},"Microsoft a d'abord exploré cela avec le support Cooperative Vector. En 2026, ce travail a évolué vers une conception DirectX Linear Algebra plus large qui prend en charge les opérations vecteur-matrice et matrice-matrice.",{},{"id":585,"data":586,"type":544,"tunes":588},"p-why-3",{"text":587},"Cela compte parce que différentes tâches de rendu neuronal ont différentes formes. Un petit modèle évaluant le comportement des matériaux par pixel n'est pas la même charge de travail qu'un grand graphe de super-résolution ou de débruitage.",{},{"id":590,"data":591,"type":572,"tunes":593},"h-two-level",{"text":592,"level":47},"Le modèle ML DirectX à deux niveaux",{},{"id":595,"data":596,"type":612,"tunes":613},"two-level-flow",{"steps":597,"title":610,"orientation":611},[598,601,604,607],{"label":599,"description":600},"1. ML au niveau du shader","De petites charges de travail neuronales ou d'algèbre linéaire s'exécutent directement depuis HLSL aux côtés du code de shader traditionnel.",{"label":602,"description":603},"2. DX Linear Algebra","Le shader peut demander des opérations vectorielles et matricielles accélérées par le matériel au lieu d'implémenter manuellement chaque primitive ML.",{"label":605,"description":606},"3. ML au niveau du modèle","Les réseaux neuronaux plus volumineux sont représentés comme des graphes de calcul complets plutôt que comme des fragments de shader écrits à la main.",{"label":608,"description":609},"4. DirectX Compute Graph Compiler","Le chemin de compilation prévu par Microsoft analyse et abaisse ces graphes en charges de travail GPU optimisées intégrées aux files d'attente et listes de commandes D3D12.","Deux façons dont le ML peut entrer dans le pipeline graphique DirectX","auto","processFlow",{},{"id":615,"data":616,"type":552,"tunes":620},"simple-diff",{"body":617,"title":618,"variant":619},"\u003Cstrong>DX Linear Algebra :\u003C\u002Fstrong> placer de petits calculs ML dans le shader.\u003Cbr>\u003Cstrong>Compute Graph Compiler :\u003C\u002Fstrong> apporter un modèle ML plus volumineux dans le moteur sous forme de graphe.","La différence simple","success",{},{"id":622,"data":623,"type":572,"tunes":625},"h-linalg",{"text":624,"level":47},"Ce que DX Linear Algebra apporte réellement à un shader",{},{"id":627,"data":628,"type":544,"tunes":630},"p-linalg-1",{"text":629},"Le HLSL traditionnel est construit autour d'opérations graphiques et de calcul. Les charges de travail neuronales reposent fortement sur l'algèbre linéaire : vecteurs, matrices, multiplication, accumulation et dispositions de données optimisées pour ces opérations.",{},{"id":632,"data":633,"type":544,"tunes":635},"p-linalg-2",{"text":634},"La préversion de Shader Model 6.10 ajoute des API matricielles de première classe afin que les développeurs puissent exprimer ces charges de travail plus directement et laisser le pilote les mapper sur du matériel spécialisé.",{},{"id":637,"data":638,"type":544,"tunes":640},"p-linalg-3",{"text":639},"La préversion d'avril 2026 de Microsoft décrit explicitement cela comme un chemin unifié pour le rendu neuronal, le ML et les charges de travail de traitement d'image plutôt que comme une fonctionnalité purement graphique.",{},{"id":642,"data":643,"type":666,"tunes":667},"math-table",{"rows":644,"title":657,"layout":658,"columns":659},[645,649,653],{"id":646,"label":647,"values":648},"work","Travail typique",[13,13],{"id":650,"label":651,"values":652},"hardware","Chemin matériel",[13,13],{"id":654,"label":655,"values":656},"code","Expression du développeur",[13,13],"Mathématiques de shader traditionnelles vs mathématiques de shader orientées ML","table",[660,663],{"id":661,"label":662},"traditional","Focus traditionnel du shader",{"id":664,"label":665},"ml","Focus orienté ML","comparison",{},{"id":669,"data":670,"type":572,"tunes":672},"h-coop",{"text":671,"level":47},"Pourquoi Cooperative Vector n'était que le début",{},{"id":674,"data":675,"type":544,"tunes":677},"p-coop-1",{"text":676},"Cooperative Vector permettait aux threads de shader de demander un travail vecteur-matrice que les pilotes pouvaient mapper sur du matériel spécialisé. C'était utile pour les charges de travail hautement parallèles par pixel.",{},{"id":679,"data":680,"type":544,"tunes":682},"p-coop-2",{"text":681},"Microsoft a ensuite conclu que de nombreuses charges de travail importantes de ML nécessitent plus que des opérations vecteur-matrice. La super-résolution, le débruitage, la reconstruction temporelle, les modèles d'image plus grands et l'inférence générale peuvent nécessiter des opérations matrice-matrice et un travail partagé entre de nombreux threads.",{},{"id":684,"data":685,"type":544,"tunes":687},"p-coop-3",{"text":686},"DX Linear Algebra élargit donc le modèle au lieu de traiter Cooperative Vector comme l'abstraction finale.",{},{"id":689,"data":690,"type":572,"tunes":692},"h-usecases",{"text":691,"level":47},"Que peut-on réellement exécuter dans un shader ?",{},{"id":694,"data":695,"type":544,"tunes":697},"p-use-1",{"text":696},"Les charges de travail ML au niveau du shader les plus intéressantes sont suffisamment petites pour s'exécuter à proximité des données graphiques sur lesquelles elles opèrent.",{},{"id":699,"data":700,"type":658,"tunes":721},"usecases-table",{"content":701,"stretched":720,"withHeadings":15},[702,705,708,711,714,717],[703,704],"Charge de travail","Pourquoi le ML au niveau du shader convient",[706,707],"Compression de texture neuronale","Un petit réseau peut reconstruire les informations de texture près du point où le shader en a besoin",[709,710],"Évaluation de matériau neuronal","Une fonction apprise peut remplacer ou augmenter des calculs de matériau coûteux écrits à la main",[712,713],"Mise en cache de radiance neuronale","L'inférence par pixel ou locale peut estimer les informations d'éclairage à partir du comportement appris de la scène",[715,716],"Petits noyaux de débruitage\u002Freconstruction","Les opérations ML peuvent se situer directement à côté de l'étape de rendu qu'elles améliorent",[718,719],"Inférence de traitement d'image","Les opérations matricielles peuvent être intégrées au traitement GPU sans runtime externe séparé",false,{},{"id":723,"data":724,"type":572,"tunes":726},"h-texture",{"text":725,"level":47},"Pourquoi cela importe pour la mémoire de texture",{},{"id":728,"data":729,"type":544,"tunes":731},"p-tex-1",{"text":730},"L'un des exemples récurrents de Microsoft est la compression de texture neuronale.",{},{"id":733,"data":734,"type":544,"tunes":736},"p-tex-2",{"text":735},"Au lieu de stocker chaque canal de texture à une fidélité conventionnelle, un jeu peut stocker une représentation plus compacte et utiliser un petit réseau neuronal pour reconstruire les détails pendant le rendu.",{},{"id":738,"data":739,"type":544,"tunes":741},"p-tex-3",{"text":740},"Cela échange un certain travail d'inférence GPU contre une pression de stockage ou de mémoire réduite. Le bénéfice exact dépend de la technique et du matériel, mais le changement architectural est important : certains détails visuels peuvent devenir du calcul plutôt que des données stockées.",{},{"id":743,"data":744,"type":749,"tunes":750},"ref-vram",{"url":745,"title":746,"excerpt":747,"ctaLabel":748},"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story","L'utilisation de la VRAM n'est pas une exigence de VRAM : pourquoi un compteur de mémoire plein ne raconte pas toute l'histoire","Un guide pratique sur la capacité de VRAM, les budgets de résidence, les ensembles de travail et pourquoi la pression mémoire est plus compliquée qu'un seul chiffre d'utilisation.","Lire le guide VRAM","referralArticle",{},{"id":752,"data":753,"type":572,"tunes":755},"h-models",{"text":754,"level":47},"Pourquoi les modèles complets nécessitent un chemin différent",{},{"id":757,"data":758,"type":544,"tunes":760},"p-models-1",{"text":759},"Écrire à la main quelques opérations matricielles en HLSL est pratique pour de petites fonctions neuronales. Cela devient beaucoup moins pratique lorsque la charge de travail est un modèle moderne complet avec de nombreuses couches, dépendances et tenseurs intermédiaires.",{},{"id":762,"data":763,"type":544,"tunes":765},"p-models-2",{"text":764},"Le DirectX Compute Graph Compiler de Microsoft est conçu pour cette classe plus large de charges de travail.",{},{"id":767,"data":768,"type":544,"tunes":770},"p-models-3",{"text":769},"Au lieu de réécrire le modèle en code de shader personnalisé, le compilateur peut accepter un graphe de calcul, analyser l'ensemble du graphe, planifier la mémoire, fusionner les opérations et abaisser le résultat en travail GPU qui s'intègre à DirectX 12.",{},{"id":772,"data":773,"type":572,"tunes":775},"h-boundary",{"text":774,"level":47},"La frontière shader-ou-graphe",{},{"id":777,"data":778,"type":666,"tunes":808},"boundary-table",{"rows":779,"title":800,"layout":658,"columns":801},[780,784,788,792,796],{"id":781,"label":782,"values":783},"size","Taille du modèle",[13,13],{"id":785,"label":786,"values":787},"location","Style d'exécution",[13,13],{"id":789,"label":790,"values":791},"authoring","Écriture",[13,13],{"id":793,"label":794,"values":795},"optimization","Portée de l'optimisation",[13,13],{"id":797,"label":798,"values":799},"use","Utilisation typique",[13,13],"Quand la charge de travail ML appartient au HLSL vs un compilateur de modèle",[802,805],{"id":803,"label":804},"shader","Chemin au niveau du shader",{"id":806,"label":807},"graph","Chemin au niveau du modèle",{},{"id":810,"data":811,"type":572,"tunes":813},"h-crossvendor",{"text":812,"level":47},"Pourquoi le support multi-fournisseurs importe plus qu'une autre fonctionnalité d'IA",{},{"id":815,"data":816,"type":544,"tunes":818},"p-cross-1",{"text":817},"AMD, Intel, NVIDIA et Qualcomm exposent différentes architectures GPU et différentes formes d'accélération matricielle dédiée.",{},{"id":820,"data":821,"type":544,"tunes":823},"p-cross-2",{"text":822},"Une abstraction DirectX donne à Microsoft et aux fournisseurs de pilotes un endroit pour traduire le HLSL courant ou le ML au niveau du graphe vers le bon chemin matériel.",{},{"id":825,"data":826,"type":544,"tunes":828},"p-cross-3",{"text":827},"Cela ne rend pas tous les GPU également rapides, mais cela peut réduire le besoin pour un moteur de jeu d'implémenter une API de rendu neuronal complètement différente pour chaque fournisseur.",{},{"id":830,"data":831,"type":552,"tunes":834},"portability-note",{"body":832,"title":833,"variant":559},"La partie intéressante n'est pas que DirectX puisse « exécuter de l'IA ». Les GPU le pouvaient déjà. Le changement de plateforme est que \u003Cstrong>le ML devient exprimable via une API graphique et une chaîne d'outils communes\u003C\u002Fstrong>.","La portabilité est la véritable histoire de plateforme",{},{"id":836,"data":837,"type":572,"tunes":839},"h-support",{"text":838,"level":47},"Quel matériel prend en charge l'aperçu actuel ?",{},{"id":841,"data":842,"type":544,"tunes":844},"p-support-1",{"text":843},"La réponse dépend de l'opération d'algèbre linéaire spécifique et du pilote d'aperçu.",{},{"id":846,"data":847,"type":544,"tunes":849},"p-support-2",{"text":848},"Le tableau de prise en charge de l'aperçu du SDK Agility 1.721 de Microsoft répertorie LinAlg VectorAccumulate pour le matériel AMD Radeon RX 9000 Series, le matériel Intel Xe2 ou plus récent via un pilote à venir, et le matériel NVIDIA RTX via le chemin d'aperçu pris en charge.",{},{"id":851,"data":852,"type":544,"tunes":854},"p-support-3",{"text":853},"Il s'agit d'une prise en charge à l'ère de l'aperçu, pas d'une garantie de vente au détail universelle. La prise en charge du matériel et des pilotes peut changer avant la finalisation.",{},{"id":856,"data":857,"type":572,"tunes":859},"h-infra",{"text":858,"level":47},"Le rendu neuronal devient une infrastructure",{},{"id":861,"data":862,"type":544,"tunes":864},"p-infra-1",{"text":863},"DLSS, FSR et d'autres technologies graphiques neuronales sont souvent présentées comme des fonctionnalités de marque visibles dans le menu des paramètres d'un jeu.",{},{"id":866,"data":867,"type":544,"tunes":869},"p-infra-2",{"text":868},"L'algèbre linéaire DirectX indique un changement plus profond. L'opération neuronale peut devenir un détail d'implémentation interne au sein du moteur de rendu plutôt qu'une simple fonctionnalité de post-traitement optionnelle.",{},{"id":871,"data":872,"type":544,"tunes":874},"p-infra-3",{"text":873},"Un développeur pourrait utiliser le ML pour les textures, les matériaux, l'éclairage, la reconstruction ou d'autres fonctions locales sans exposer chacune d'elles comme une option d'IA destinée au consommateur.",{},{"id":876,"data":877,"type":749,"tunes":882},"ref-dlss5",{"url":878,"title":879,"excerpt":880,"ctaLabel":881},"https:\u002F\u002Ffigure.rocks\u002Ffr\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes","DLSS 5 n'est pas qu'une mise à l'échelle : ce que le rendu neuronal guidé par la 3D change réellement","Comment le rendu neuronal dépasse la mise à l'échelle et les images générées pour s'étendre à la reconstruction des matériaux et de l'éclairage au sein du pipeline graphique.","Lire le guide du rendu neuronal DLSS 5",{},{"id":884,"data":885,"type":572,"tunes":887},"h-stack",{"text":886,"level":47},"La pile de rendu neuronal devient stratifiée",{},{"id":889,"data":890,"type":612,"tunes":908},"stack-flow",{"steps":891,"title":907,"orientation":611},[892,895,898,901,904],{"label":893,"description":894},"Travail traditionnel du moteur","La simulation, la géométrie, la visibilité et le rendu de base restent des responsabilités standard du moteur.",{"label":896,"description":897},"Shaders neuronaux en ligne","De petites fonctions apprises reconstruisent les textures, les matériaux ou l'éclairage dans HLSL.",{"label":899,"description":900},"Graphes ML plus grands","Les modèles complets de reconstruction ou d'inférence s'exécutent via un chemin DirectX au niveau du graphe.",{"label":902,"description":903},"Mappage matériel du fournisseur","Les pilotes mappent les opérations DirectX courantes sur les unités IA et matricielles spécialisées du GPU.",{"label":905,"description":906},"Visibilité PIX","Le travail graphique et le travail ML peuvent être profilés ensemble au lieu de résider dans des runtimes opaques séparés.","Un futur pipeline de jeu DirectX possible",{},{"id":910,"data":911,"type":572,"tunes":913},"h-pix",{"text":912,"level":47},"Pourquoi le profilage unifié est important",{},{"id":915,"data":916,"type":544,"tunes":918},"p-pix-1",{"text":917},"Une charge de travail neuronale qui améliore la qualité de l'image peut quand même nuire à un jeu si elle consomme de manière inattendue du temps de frame, de la bande passante mémoire ou de la VRAM.",{},{"id":920,"data":921,"type":544,"tunes":923},"p-pix-2",{"text":922},"Microsoft inclut explicitement la visibilité PIX unifiée comme partie de l'orientation du Compute Graph Compiler. Cela compte car les développeurs doivent voir le travail graphique et le travail ML dans la même capture de frame.",{},{"id":925,"data":926,"type":544,"tunes":928},"p-pix-3",{"text":927},"Si le rendu neuronal devient une infrastructure, il doit être mesurable comme n'importe quelle autre étape de rendu.",{},{"id":930,"data":931,"type":572,"tunes":933},"h-not-everything",{"text":932,"level":47},"Cela ne signifie pas que chaque shader deviendra un réseau de neurones",{},{"id":935,"data":936,"type":544,"tunes":938},"p-not-1",{"text":937},"Les mathématiques traditionnelles des shaders restent efficaces, déterministes et faciles à appréhender pour de nombreuses charges de travail.",{},{"id":940,"data":941,"type":544,"tunes":943},"p-not-2",{"text":942},"Une fonction neuronale a du sens lorsqu'elle peut approximer ou reconstruire quelque chose de coûteux plus efficacement, compresser des données, ou produire une qualité qui nécessiterait autrement trop de calcul ou de mémoire conventionnels.",{},{"id":945,"data":946,"type":544,"tunes":948},"p-not-3",{"text":947},"L'architecture appropriée restera hybride.",{},{"id":950,"data":951,"type":572,"tunes":953},"h-test",{"text":952,"level":47},"Le test de valeur du shader neuronal",{},{"id":955,"data":956,"type":612,"tunes":977},"value-test",{"steps":957,"title":976,"orientation":611},[958,961,964,967,970,973],{"label":959,"description":960},"1. Identifier l'opération traditionnelle coûteuse","Quel coût de calcul, de bande passante, de mémoire ou de stockage essayez-vous de remplacer ?",{"label":962,"description":963},"2. Définir le substitut neuronal","Que peut reconstruire, prédire ou compresser un petit modèle ?",{"label":965,"description":966},"3. Mesurer le coût d'inférence","Le modèle ML lui-même consomme du temps GPU, de la mémoire et de la bande passante.",{"label":968,"description":969},"4. Mesurer la stabilité de la qualité","Recherchez les artefacts temporels, les erreurs de reconstruction et les cas d'échec.",{"label":971,"description":972},"5. Mesurer les économies nettes","La technique n'est utile que si le coût traditionnel évité vaut le coût ML ajouté.",{"label":974,"description":975},"6. Tester sur différents fournisseurs","Une abstraction DirectX aide à la portabilité, mais les performances matérielles réelles diffèrent toujours.","Quand est-ce que l'intégration du ML dans le pipeline de rendu a réellement du sens ?",{},{"id":979,"data":980,"type":572,"tunes":982},"h-gamers",{"text":981,"level":47},"Ce que cela signifie pour les joueurs",{},{"id":984,"data":985,"type":544,"tunes":987},"p-gamers-1",{"text":986},"Les joueurs ne verront peut-être jamais un commutateur « DX Linear Algebra » dans un menu graphique.",{},{"id":989,"data":990,"type":544,"tunes":992},"p-gamers-2",{"text":991},"L'impact probable est indirect : des empreintes de textures plus petites, une meilleure reconstruction de l'éclairage, des graphismes neuronaux plus efficaces, ou de nouvelles techniques visuelles qui deviennent pratiques parce que le moteur peut accéder à l'accélération matricielle via un chemin standard.",{},{"id":994,"data":995,"type":544,"tunes":997},"p-gamers-3",{"text":996},"La fonctionnalité est plus importante en tant qu'infrastructure qu'en tant que marque.",{},{"id":999,"data":1000,"type":572,"tunes":1002},"h-change",{"text":1001,"level":47},"Qu'est-ce qui changerait cette réponse ?",{},{"id":1004,"data":1005,"type":544,"tunes":1007},"p-change-1",{"text":1006},"La plus grande incertitude réside dans la forme finale de vente au détail de ces API. DX Linear Algebra reste en préversion, et le Compute Graph Compiler n'a pas encore atteint une large disponibilité en version commerciale.",{},{"id":1009,"data":1010,"type":544,"tunes":1012},"p-change-2",{"text":1011},"La prise en charge matérielle finale, les détails de l'API, le comportement du compilateur et l'adoption par les moteurs peuvent changer avant que ces systèmes ne deviennent une infrastructure normale pour les jeux commercialisés.",{},{"id":1014,"data":1015,"type":544,"tunes":1017},"p-change-3",{"text":1016},"Si les principaux moteurs adoptent directement les abstractions, les shaders neuronaux pourraient devenir beaucoup plus courants sans que chaque équipe de jeu implémente chaque technique à partir de zéro.",{},{"id":1019,"data":1020,"type":572,"tunes":1022},"h-limit",{"text":1021,"level":47},"Limitations",{},{"id":1024,"data":1025,"type":544,"tunes":1027},"p-limit-1",{"text":1026},"Cet article explique l'architecture DirectX documentée par Microsoft et les API en préversion. Il ne prétend pas que DX Linear Algebra améliore actuellement les performances dans chaque jeu ni que le Compute Graph Compiler est un produit commercial fini.",{},{"id":1029,"data":1030,"type":544,"tunes":1032},"p-limit-2",{"text":1031},"Les exemples de Microsoft décrivent la capacité et l'utilisation prévue. Les avantages réels dépendent du modèle, de l'intégration au moteur, de l'architecture GPU, des pilotes et de la charge de travail.",{},{"id":1034,"data":1035,"type":572,"tunes":1037},"h-conclusion",{"text":1036,"level":47},"Conclusion",{},{"id":1039,"data":1040,"type":544,"tunes":1042},"p-conc-1",{"text":1041},"Le changement important de DirectX n'est pas une nouvelle case à cocher appelée IA.",{},{"id":1044,"data":1045,"type":544,"tunes":1047},"p-conc-2",{"text":1046},"C'est que les mathématiques d'apprentissage automatique intègrent le modèle de programmation graphique lui-même. De petites fonctions neuronales peuvent résider dans les shaders, des modèles plus grands peuvent évoluer vers une compilation au niveau du graphe, et la même chaîne d'outils DirectX peut exposer ces charges de travail sur plusieurs fournisseurs de GPU.",{},{"id":1049,"data":1050,"type":544,"tunes":1052},"p-conc-3",{"text":1051},"C'est ainsi que le rendu neuronal cesse d'être une fonctionnalité de marque pour devenir une partie de l'infrastructure de rendu.",{},{"id":1054,"data":1055,"type":572,"tunes":1057},"h-faq",{"text":1056,"level":47},"FAQ",{},{"id":1059,"data":1060,"type":1059,"tunes":1087},"faq",{"items":1061,"title":1086},[1062,1066,1070,1074,1078,1082],{"id":1063,"answer":1064,"question":1065},"faq1","C'est un ensemble de fonctionnalités DirectX\u002FHLSL pour les opérations vectorielles et matricielles accélérées par le matériel, utilisées par l'apprentissage automatique, le rendu neuronal et les charges de travail de traitement d'images.","Qu'est-ce que l'algèbre linéaire DirectX ?",{"id":1067,"answer":1068,"question":1069},"faq2","Une description simple et utile est un shader qui inclut une fonction neuronale apprise ou une étape d'inférence ML dans le cadre de son travail graphique.","Qu'est-ce qu'un shader neuronal ?",{"id":1071,"answer":1072,"question":1073},"faq3","En septembre 2026, elle reste dans la voie de prévisualisation Shader Model 6.10 \u002F Agility SDK.","L'algèbre linéaire DX est-elle déjà une fonctionnalité DirectX standard en vente au détail ?",{"id":1075,"answer":1076,"question":1077},"faq4","C'est l'API de compilateur ML au niveau du modèle annoncée par Microsoft, destinée à prendre des graphes de calcul plus grands et à les abaisser en charges de travail GPU optimisées intégrées à D3D12.","Qu'est-ce que le compilateur de graphes de calcul DirectX ?",{"id":1079,"answer":1080,"question":1081},"faq5","Les petites fonctions s'adaptent bien à l'écriture au niveau du shader, tandis que les grands modèles bénéficient d'une optimisation globale du graphe, d'une planification de la mémoire et d'une fusion d'opérateurs.","Pourquoi ne pas exécuter chaque modèle ML directement en HLSL ?",{"id":1083,"answer":1084,"question":1085},"faq6","Pas directement. DirectX fournit une infrastructure de bas niveau que les fournisseurs et les développeurs peuvent utiliser pour les graphiques neuronaux. Les technologies de marque peuvent toujours implémenter leurs propres modèles et stratégies d'intégration.","Cela remplacera-t-il DLSS ou FSR ?","Algèbre linéaire DirectX et shaders neuronaux",{},{"id":1089,"data":1090,"type":572,"tunes":1092},"h-glossary",{"text":1091,"level":47},"Glossaire",{},{"id":1094,"data":1095,"type":1094,"tunes":1126},"glossary",{"title":1096,"entries":1097},"Termes clés du ML DirectX",[1098,1102,1106,1110,1114,1118,1122],{"term":1099,"anchor":1100,"definition":1101},"Algèbre linéaire DX","dx-linear-algebra","API DirectX\u002FHLSL pour les opérations vectorielles et matricielles accélérées destinées au rendu neuronal, au ML et aux charges de travail de traitement d'images.",{"term":1103,"anchor":1104,"definition":1105},"Vecteur coopératif","cooperative-vector","Une approche DirectX antérieure pour les opérations vectorielles-matricielles accélérées dans les shaders qui a contribué à établir la voie du rendu neuronal au niveau du shader.",{"term":1107,"anchor":1108,"definition":1109},"Shader Model 6.10","shader-model-6-10","La génération de modèle de shader en prévisualisation contenant les API matricielles actuelles d'algèbre linéaire DirectX.",{"term":1111,"anchor":1112,"definition":1113},"Compilateur de graphes de calcul DirectX","compute-graph-compiler","L'API de compilateur annoncée par Microsoft pour optimiser et exécuter des graphes de calcul ML plus grands en tant que charges de travail GPU DirectX natives.",{"term":1115,"anchor":1116,"definition":1117},"Shader neuronal","neural-shader","Un terme pratique pour un shader qui effectue une inférence apprise ou un calcul neuronal dans le cadre du traitement graphique.",{"term":1119,"anchor":1120,"definition":1121},"Frontière shader-ou-graphe","shader-or-graph-boundary","Un modèle Figure Rocks pour décider si une charge de travail ML doit être de petites mathématiques de shader en ligne ou un graphe de calcul plus grand au niveau du modèle.",{"term":1123,"anchor":1124,"definition":1125},"Test de valeur du shader neuronal","neural-shader-value-test","Un flux de travail Figure Rocks pour décider si le coût de calcul ou de mémoire traditionnel évité par une technique neuronale vaut son coût d'inférence et ses compromis de qualité.",{},{"id":1128,"data":1129,"type":572,"tunes":1131},"h-sources",{"text":1130,"level":47},"Sources principales",{},{"id":1133,"data":1134,"type":1140,"tunes":1141},"src-ms-ml-era",{"link":1135,"meta":1136},"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fevolving-directx-for-the-ml-era-on-windows\u002F",{"image":1137,"title":1138,"description":1139},{"url":13},"Microsoft DirectX — Faire évoluer DirectX pour l'ère du ML sur Windows","Aperçu officiel de l'architecture GDC 2026 couvrant le ML au niveau du shader, l'algèbre linéaire DX et le compilateur de graphes de calcul DirectX.","linkTool",{},{"id":1143,"data":1144,"type":1140,"tunes":1150},"src-ms-linalg",{"link":1145,"meta":1146},"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fd3d12-linalg-preview\u002F",{"image":1147,"title":1148,"description":1149},{"url":13},"Microsoft DirectX — Aperçu de la matrice LinAlg D3D12","Aperçu officiel d'avril 2026 expliquant les API unifiées d'algèbre linéaire, les opérations matricielles et la motivation du rendu neuronal.",{},{"id":1152,"data":1153,"type":1140,"tunes":1159},"src-ms-agility721",{"link":1154,"meta":1155},"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fannouncing-agilitysdk-721-preview-and-more-shader-model-6-10-features\u002F",{"image":1156,"title":1157,"description":1158},{"url":13},"Microsoft DirectX — Aperçu du SDK Agility 1.721","Version officielle de mai 2026 documentant les mises à jour d'algèbre linéaire de Shader Model 6.10 et la prise en charge matérielle en prévisualisation.",{},{"id":1161,"data":1162,"type":1140,"tunes":1168},"src-ms-gdc",{"link":1163,"meta":1164},"https:\u002F\u002Fdeveloper.microsoft.com\u002Fen-us\u002Fgames\u002Farticles\u002F2026\u002F03\u002Fgdc-2026-evolving-directx-for-ml-era-on-windows\u002F",{"image":1165,"title":1166,"description":1167},{"url":13},"Microsoft Game Dev — GDC 2026 : Faire évoluer DirectX pour l'ère du ML","Résumé officiel de Microsoft Game Dev expliquant le ML au niveau du shader et du modèle et le rôle du compilateur de graphes de calcul.",{},{"id":1170,"data":1171,"type":1140,"tunes":1177},"src-ms-coop",{"link":1172,"meta":1173},"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fcooperative-vector\u002F",{"image":1174,"title":1175,"description":1176},{"url":13},"Microsoft DirectX — Vecteur coopératif D3D12","Contexte officiel sur les opérations vectorielles\u002Fmatricielles accélérées par le matériel et le rendu neuronal directement à partir des threads de shader.",{},"2.31","DirectX dépasse le cadre des shaders graphiques traditionnels. Microsoft ajoute l'algèbre linéaire accélérée par le matériel directement à HLSL et un chemin distinct pour les modèles ML plus volumineux, jetant les bases des textures neuronales, des matériaux appris, de l'éclairage neuronal et d'autres techniques de rendu pilotées par l'IA.","\u002Fuploads\u002F2026\u002F09\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games-1790378019857-utigyk.webp","directx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games-1790378019857-utigyk","PUBLISHED","2026-09-25T17:12:00.000Z","2026-09-25T23:12:37.728Z","2026-09-26T06:45:35.226Z",{"en":1187,"de":1188,"sr":1189,"es":1190,"fr":1191,"it":1192,"ru":1193,"zh":1194},"\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fde\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fsr\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fes\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Ffr\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fit\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fru\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games","\u002Fzh\u002Fblog\u002Fdirectx-is-becoming-an-ml-platform-what-linear-algebra-and-neural-shaders-mean-for-future-games",[1196,1200,1204,1208,1212],{"id":1197,"name":1198,"slug":1199},251,"Flou et persistance","blur-and-persistence",{"id":1201,"name":1202,"slug":1203},252,"Overdrive et Smearing","overdrive-and-smearing",{"id":1205,"name":1206,"slug":1207},253,"Rafraîchissement et clarté","refresh-and-clarity",{"id":1209,"name":1210,"slug":1211},430,"Animal Crossing","animal-crossing",{"id":1213,"name":1214,"slug":1215},220,"Le design au service de l'usage","design-that-serves-use",{"id":283,"login":1217,"email":1218,"displayName":1219},"aleksandar","aleksandar@stajic.de","Aleksandar Stajic",[1221,1731],{"lang":8,"title":1222,"content":1223,"contentJson":1224,"excerpt":1730},"DirectX Is Becoming an ML Platform: What Linear Algebra and Neural Shaders Mean for Future Games","{\"time\":1790405124627,\"blocks\":[{\"id\":\"intro\",\"type\":\"paragraph\",\"data\":{\"text\":\"DirectX is no longer only a graphics API that sends traditional shaders to the GPU. Microsoft is adding machine-learning primitives directly to HLSL and a second path for running larger ML graphs inside the DirectX ecosystem. That changes where neural rendering can live inside future PC games.\"},\"tunes\":{}},{\"id\":\"direct\",\"type\":\"callout\",\"data\":{\"variant\":\"info\",\"title\":\"Direct answer\",\"body\":\"\u003Cstrong>DirectX is becoming an ML-capable graphics platform.\u003C\u002Fstrong> Small neural workloads can run directly inside shaders through DX Linear Algebra, while larger models are being targeted by the DirectX Compute Graph Compiler. The goal is to let game engines use GPU AI hardware without building a separate vendor-specific path for every technique.\"},\"tunes\":{}},{\"id\":\"status-note\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"Current status\",\"body\":\"As of September 2026, \u003Cstrong>DX Linear Algebra is still a preview technology\u003C\u002Fstrong> in the Shader Model 6.10 \u002F Agility SDK preview path. Microsoft announced the DirectX Compute Graph Compiler for private preview rather than as a broadly shipping retail feature. This article explains the architecture and direction, not a claim that every current game can use it today.\"},\"tunes\":{}},{\"id\":\"toc\",\"type\":\"tableOfContents\",\"data\":{\"title\":\"Contents\",\"minLevel\":2,\"maxLevel\":3},\"tunes\":{}},{\"id\":\"h-why\",\"type\":\"header\",\"data\":{\"text\":\"Why neural rendering needs new DirectX primitives\",\"level\":2},\"tunes\":{}},{\"id\":\"p-why-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Modern GPUs already contain specialized hardware for matrix operations used by machine learning. The problem for a game developer is not only whether that hardware exists, but how to access it efficiently from a real-time graphics pipeline.\"},\"tunes\":{}},{\"id\":\"p-why-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft first explored this with Cooperative Vector support. In 2026, that work evolved into a broader DirectX Linear Algebra design that supports both vector-matrix and matrix-matrix operations.\"},\"tunes\":{}},{\"id\":\"p-why-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That matters because different neural-rendering jobs have different shapes. A tiny model evaluating material behavior per pixel is not the same workload as a large super-resolution or denoising graph.\"},\"tunes\":{}},{\"id\":\"h-two-level\",\"type\":\"header\",\"data\":{\"text\":\"The Two-Level DirectX ML Model\",\"level\":2},\"tunes\":{}},{\"id\":\"two-level-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"Two ways ML can enter the DirectX graphics pipeline\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Shader-level ML\",\"description\":\"Small neural or linear-algebra workloads execute directly from HLSL alongside traditional shader code.\"},{\"label\":\"2. DX Linear Algebra\",\"description\":\"The shader can request hardware-accelerated vector and matrix operations instead of manually implementing every ML primitive.\"},{\"label\":\"3. Model-level ML\",\"description\":\"Larger neural networks are represented as complete computation graphs rather than hand-written shader fragments.\"},{\"label\":\"4. DirectX Compute Graph Compiler\",\"description\":\"Microsoft's planned compiler path analyzes and lowers those graphs into optimized GPU workloads integrated with D3D12 queues and command lists.\"}]},\"tunes\":{}},{\"id\":\"simple-diff\",\"type\":\"callout\",\"data\":{\"variant\":\"success\",\"title\":\"The simple difference\",\"body\":\"\u003Cstrong>DX Linear Algebra:\u003C\u002Fstrong> put small ML math inside the shader.\u003Cbr>\u003Cstrong>Compute Graph Compiler:\u003C\u002Fstrong> bring a larger ML model into the engine as a graph.\"},\"tunes\":{}},{\"id\":\"h-linalg\",\"type\":\"header\",\"data\":{\"text\":\"What DX Linear Algebra actually gives a shader\",\"level\":2},\"tunes\":{}},{\"id\":\"p-linalg-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Traditional HLSL is built around graphics and compute operations. Neural workloads rely heavily on linear algebra: vectors, matrices, multiplication, accumulation and data layouts optimized for those operations.\"},\"tunes\":{}},{\"id\":\"p-linalg-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The Shader Model 6.10 preview adds first-class matrix APIs so developers can express those workloads more directly and let the driver map them to specialized hardware.\"},\"tunes\":{}},{\"id\":\"p-linalg-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft's April 2026 preview explicitly describes this as a unified path for neural rendering, ML and image-processing workloads rather than a graphics-only feature.\"},\"tunes\":{}},{\"id\":\"math-table\",\"type\":\"comparison\",\"data\":{\"title\":\"Traditional shader math vs ML-oriented shader math\",\"layout\":\"table\",\"columns\":[{\"id\":\"traditional\",\"label\":\"Traditional shader focus\"},{\"id\":\"ml\",\"label\":\"ML-oriented focus\"}],\"rows\":[{\"id\":\"work\",\"label\":\"Typical work\",\"values\":[\"\",\"\"]},{\"id\":\"hardware\",\"label\":\"Hardware path\",\"values\":[\"\",\"\"]},{\"id\":\"code\",\"label\":\"Developer expression\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-coop\",\"type\":\"header\",\"data\":{\"text\":\"Why Cooperative Vector was only the beginning\",\"level\":2},\"tunes\":{}},{\"id\":\"p-coop-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Cooperative Vector allowed shader threads to request vector-matrix work that drivers could map to specialized hardware. That was useful for highly parallel per-pixel workloads.\"},\"tunes\":{}},{\"id\":\"p-coop-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft later concluded that many important ML workloads need more than vector-matrix operations. Super resolution, denoising, temporal reconstruction, larger image models and general inference can require matrix-matrix operations and shared work across many threads.\"},\"tunes\":{}},{\"id\":\"p-coop-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"DX Linear Algebra therefore broadens the model instead of treating Cooperative Vector as the final abstraction.\"},\"tunes\":{}},{\"id\":\"h-usecases\",\"type\":\"header\",\"data\":{\"text\":\"What can actually run inside a shader?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-use-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The most interesting shader-level ML workloads are small enough to execute close to the graphics data they operate on.\"},\"tunes\":{}},{\"id\":\"usecases-table\",\"type\":\"table\",\"data\":{\"withHeadings\":true,\"stretched\":false,\"content\":[[\"Workload\",\"Why shader-level ML fits\"],[\"Neural texture compression\",\"A small network can reconstruct texture information near the point where the shader needs it\"],[\"Neural material evaluation\",\"A learned function can replace or augment expensive hand-authored material math\"],[\"Neural radiance caching\",\"Per-pixel or local inference can estimate lighting information from learned scene behavior\"],[\"Small denoising\u002Freconstruction kernels\",\"ML operations can sit directly beside the rendering stage they improve\"],[\"Image-processing inference\",\"Matrix operations can be embedded into GPU processing without a separate external runtime\"]]},\"tunes\":{}},{\"id\":\"h-texture\",\"type\":\"header\",\"data\":{\"text\":\"Why this matters for texture memory\",\"level\":2},\"tunes\":{}},{\"id\":\"p-tex-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"One of Microsoft's recurring examples is neural texture compression.\"},\"tunes\":{}},{\"id\":\"p-tex-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Instead of storing every texture channel at conventional fidelity, a game can store a more compact representation and use a small neural network to reconstruct detail during rendering.\"},\"tunes\":{}},{\"id\":\"p-tex-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That trades some GPU inference work for lower storage or memory pressure. The exact benefit depends on the technique and hardware, but the architectural change is important: some visual detail can become computation instead of stored data.\"},\"tunes\":{}},{\"id\":\"ref-vram\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story\",\"title\":\"VRAM Usage Is Not VRAM Requirement: Why a Full Memory Meter Does Not Tell the Whole Story\",\"excerpt\":\"A practical guide to VRAM capacity, residency budgets, working sets and why memory pressure is more complicated than one usage number.\",\"ctaLabel\":\"Read the VRAM guide\"},\"tunes\":{}},{\"id\":\"h-models\",\"type\":\"header\",\"data\":{\"text\":\"Why full models need a different path\",\"level\":2},\"tunes\":{}},{\"id\":\"p-models-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Hand-writing a few matrix operations in HLSL is practical for small neural functions. It becomes much less practical when the workload is a complete modern model with many layers, dependencies and intermediate tensors.\"},\"tunes\":{}},{\"id\":\"p-models-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft's DirectX Compute Graph Compiler is designed for this larger class of workload.\"},\"tunes\":{}},{\"id\":\"p-models-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"Instead of rewriting the model as custom shader code, the compiler can accept a computation graph, analyze the whole graph, plan memory, fuse operations and lower the result into GPU work that integrates with DirectX 12.\"},\"tunes\":{}},{\"id\":\"h-boundary\",\"type\":\"header\",\"data\":{\"text\":\"The Shader-or-Graph Boundary\",\"level\":2},\"tunes\":{}},{\"id\":\"boundary-table\",\"type\":\"comparison\",\"data\":{\"title\":\"When the ML workload belongs in HLSL vs a model compiler\",\"layout\":\"table\",\"columns\":[{\"id\":\"shader\",\"label\":\"Shader-level path\"},{\"id\":\"graph\",\"label\":\"Model-level path\"}],\"rows\":[{\"id\":\"size\",\"label\":\"Model size\",\"values\":[\"\",\"\"]},{\"id\":\"location\",\"label\":\"Execution style\",\"values\":[\"\",\"\"]},{\"id\":\"authoring\",\"label\":\"Authoring\",\"values\":[\"\",\"\"]},{\"id\":\"optimization\",\"label\":\"Optimization scope\",\"values\":[\"\",\"\"]},{\"id\":\"use\",\"label\":\"Typical use\",\"values\":[\"\",\"\"]}]},\"tunes\":{}},{\"id\":\"h-crossvendor\",\"type\":\"header\",\"data\":{\"text\":\"Why cross-vendor support matters more than another AI feature\",\"level\":2},\"tunes\":{}},{\"id\":\"p-cross-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"AMD, Intel, NVIDIA and Qualcomm expose different GPU architectures and different forms of dedicated matrix acceleration.\"},\"tunes\":{}},{\"id\":\"p-cross-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"A DirectX abstraction gives Microsoft and the driver vendors a place to translate common HLSL or graph-level ML into the correct hardware path.\"},\"tunes\":{}},{\"id\":\"p-cross-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That does not make every GPU equally fast, but it can reduce the need for a game engine to implement a completely different neural-rendering API for every vendor.\"},\"tunes\":{}},{\"id\":\"portability-note\",\"type\":\"callout\",\"data\":{\"variant\":\"note\",\"title\":\"Portability is the real platform story\",\"body\":\"The interesting part is not that DirectX can “run AI.” GPUs already could. The platform change is that \u003Cstrong>ML becomes expressible through a common graphics API and toolchain\u003C\u002Fstrong>.\"},\"tunes\":{}},{\"id\":\"h-support\",\"type\":\"header\",\"data\":{\"text\":\"What hardware supports the current preview?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-support-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The answer depends on the specific Linear Algebra operation and preview driver.\"},\"tunes\":{}},{\"id\":\"p-support-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft's Agility SDK 1.721 preview support table lists LinAlg VectorAccumulate for AMD Radeon RX 9000 Series hardware, Intel Xe2-or-newer hardware through an upcoming driver, and NVIDIA RTX hardware through the supported preview path.\"},\"tunes\":{}},{\"id\":\"p-support-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"This is preview-era support, not a universal retail guarantee. Hardware and driver support can change before finalization.\"},\"tunes\":{}},{\"id\":\"h-infra\",\"type\":\"header\",\"data\":{\"text\":\"Neural rendering is becoming infrastructure\",\"level\":2},\"tunes\":{}},{\"id\":\"p-infra-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"DLSS, FSR and other neural graphics technologies are often discussed as branded features visible in a game's settings menu.\"},\"tunes\":{}},{\"id\":\"p-infra-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"DirectX Linear Algebra points to a deeper change. The neural operation can become an internal implementation detail inside the renderer rather than a single optional post-processing feature.\"},\"tunes\":{}},{\"id\":\"p-infra-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"A developer could use ML for textures, materials, lighting, reconstruction or other local functions without exposing each one as a consumer-facing AI toggle.\"},\"tunes\":{}},{\"id\":\"ref-dlss5\",\"type\":\"referralArticle\",\"data\":{\"url\":\"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes\",\"title\":\"DLSS 5 Is Not Just Upscaling: What 3D-Guided Neural Rendering Actually Changes\",\"excerpt\":\"How neural rendering is moving beyond upscaling and generated frames into material and lighting reconstruction inside the graphics pipeline.\",\"ctaLabel\":\"Read the DLSS 5 neural-rendering guide\"},\"tunes\":{}},{\"id\":\"h-stack\",\"type\":\"header\",\"data\":{\"text\":\"The Neural Rendering Stack is becoming layered\",\"level\":2},\"tunes\":{}},{\"id\":\"stack-flow\",\"type\":\"processFlow\",\"data\":{\"title\":\"A possible future DirectX game pipeline\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"Traditional engine work\",\"description\":\"Simulation, geometry, visibility and base rendering remain standard engine responsibilities.\"},{\"label\":\"Inline neural shaders\",\"description\":\"Small learned functions reconstruct textures, materials or lighting inside HLSL.\"},{\"label\":\"Larger ML graphs\",\"description\":\"Full reconstruction or inference models execute through a graph-level DirectX path.\"},{\"label\":\"Vendor hardware mapping\",\"description\":\"Drivers map common DirectX operations onto the GPU's specialized AI and matrix units.\"},{\"label\":\"PIX visibility\",\"description\":\"Graphics and ML work can be profiled together instead of living in separate opaque runtimes.\"}]},\"tunes\":{}},{\"id\":\"h-pix\",\"type\":\"header\",\"data\":{\"text\":\"Why unified profiling matters\",\"level\":2},\"tunes\":{}},{\"id\":\"p-pix-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"A neural workload that improves image quality can still damage a game if it unexpectedly consumes frame time, memory bandwidth or VRAM.\"},\"tunes\":{}},{\"id\":\"p-pix-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft explicitly includes unified PIX visibility as part of the Compute Graph Compiler direction. That matters because developers need to see graphics and ML work in the same frame capture.\"},\"tunes\":{}},{\"id\":\"p-pix-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"If neural rendering becomes infrastructure, it must be measurable like any other rendering stage.\"},\"tunes\":{}},{\"id\":\"h-not-everything\",\"type\":\"header\",\"data\":{\"text\":\"This does not mean every shader will become a neural network\",\"level\":2},\"tunes\":{}},{\"id\":\"p-not-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Traditional shader math remains efficient, deterministic and easy to reason about for many workloads.\"},\"tunes\":{}},{\"id\":\"p-not-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"A neural function makes sense when it can approximate or reconstruct something expensive more efficiently, compress data, or produce quality that would otherwise require too much conventional compute or memory.\"},\"tunes\":{}},{\"id\":\"p-not-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The right architecture will remain hybrid.\"},\"tunes\":{}},{\"id\":\"h-test\",\"type\":\"header\",\"data\":{\"text\":\"The Neural Shader Value Test\",\"level\":2},\"tunes\":{}},{\"id\":\"value-test\",\"type\":\"processFlow\",\"data\":{\"title\":\"When does putting ML into the rendering pipeline actually make sense?\",\"orientation\":\"auto\",\"steps\":[{\"label\":\"1. Identify the expensive traditional operation\",\"description\":\"What compute, bandwidth, memory or storage cost are you trying to replace?\"},{\"label\":\"2. Define the neural substitute\",\"description\":\"What can a small model reconstruct, predict or compress?\"},{\"label\":\"3. Measure inference cost\",\"description\":\"The ML model itself consumes GPU time, memory and bandwidth.\"},{\"label\":\"4. Measure quality stability\",\"description\":\"Look for temporal artifacts, reconstruction errors and failure cases.\"},{\"label\":\"5. Measure net savings\",\"description\":\"The technique is useful only if the avoided traditional cost is worth the added ML cost.\"},{\"label\":\"6. Test across vendors\",\"description\":\"A DirectX abstraction helps portability, but real hardware performance still differs.\"}]},\"tunes\":{}},{\"id\":\"h-gamers\",\"type\":\"header\",\"data\":{\"text\":\"What this means for gamers\",\"level\":2},\"tunes\":{}},{\"id\":\"p-gamers-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"Gamers may never see a “DX Linear Algebra” switch in a graphics menu.\"},\"tunes\":{}},{\"id\":\"p-gamers-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"The likely impact is indirect: smaller texture footprints, better lighting reconstruction, more efficient neural graphics, or new visual techniques that become practical because the engine can access matrix acceleration through a standard path.\"},\"tunes\":{}},{\"id\":\"p-gamers-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"The feature is more important as infrastructure than as a brand.\"},\"tunes\":{}},{\"id\":\"h-change\",\"type\":\"header\",\"data\":{\"text\":\"What would change this answer?\",\"level\":2},\"tunes\":{}},{\"id\":\"p-change-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The biggest uncertainty is the final retail shape of these APIs. DX Linear Algebra remains in preview, and the Compute Graph Compiler has not yet reached broad retail availability.\"},\"tunes\":{}},{\"id\":\"p-change-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Final hardware support, API details, compiler behavior and engine adoption may change before these systems become normal shipping-game infrastructure.\"},\"tunes\":{}},{\"id\":\"p-change-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"If major engines adopt the abstractions directly, neural shaders could become much more common without individual game teams implementing every technique from scratch.\"},\"tunes\":{}},{\"id\":\"h-limit\",\"type\":\"header\",\"data\":{\"text\":\"Limitations\",\"level\":2},\"tunes\":{}},{\"id\":\"p-limit-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"This article explains Microsoft's documented DirectX architecture and preview APIs. It does not claim that DX Linear Algebra currently improves performance in every game or that the Compute Graph Compiler is a finished retail product.\"},\"tunes\":{}},{\"id\":\"p-limit-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"Microsoft's examples describe capability and intended use. Real benefits depend on the model, engine integration, GPU architecture, drivers and workload.\"},\"tunes\":{}},{\"id\":\"h-conclusion\",\"type\":\"header\",\"data\":{\"text\":\"Conclusion\",\"level\":2},\"tunes\":{}},{\"id\":\"p-conc-1\",\"type\":\"paragraph\",\"data\":{\"text\":\"The important DirectX change is not another checkbox called AI.\"},\"tunes\":{}},{\"id\":\"p-conc-2\",\"type\":\"paragraph\",\"data\":{\"text\":\"It is that machine-learning math is moving into the graphics programming model itself. Small neural functions can sit inside shaders, larger models can move toward graph-level compilation, and the same DirectX toolchain can expose those workloads across multiple GPU vendors.\"},\"tunes\":{}},{\"id\":\"p-conc-3\",\"type\":\"paragraph\",\"data\":{\"text\":\"That is how neural rendering stops being one branded feature and starts becoming part of the rendering infrastructure.\"},\"tunes\":{}},{\"id\":\"h-faq\",\"type\":\"header\",\"data\":{\"text\":\"FAQ\",\"level\":2},\"tunes\":{}},{\"id\":\"faq\",\"type\":\"faq\",\"data\":{\"title\":\"DirectX Linear Algebra and neural shaders\",\"items\":[{\"id\":\"faq1\",\"question\":\"What is DirectX Linear Algebra?\",\"answer\":\"It is a DirectX\u002FHLSL feature set for hardware-accelerated vector and matrix operations used by machine learning, neural rendering and image-processing workloads.\"},{\"id\":\"faq2\",\"question\":\"What is a neural shader?\",\"answer\":\"A useful plain-English description is a shader that includes a learned neural function or ML inference step as part of its graphics work.\"},{\"id\":\"faq3\",\"question\":\"Is DX Linear Algebra already a normal retail DirectX feature?\",\"answer\":\"As of September 2026 it remains in the Shader Model 6.10 \u002F Agility SDK preview path.\"},{\"id\":\"faq4\",\"question\":\"What is the DirectX Compute Graph Compiler?\",\"answer\":\"It is Microsoft's announced model-level ML compiler API intended to take larger computation graphs and lower them into optimized GPU workloads integrated with D3D12.\"},{\"id\":\"faq5\",\"question\":\"Why not run every ML model directly in HLSL?\",\"answer\":\"Small functions fit shader-level authoring well, while large models benefit from whole-graph optimization, memory planning and operator fusion.\"},{\"id\":\"faq6\",\"question\":\"Will this replace DLSS or FSR?\",\"answer\":\"Not directly. DirectX provides lower-level infrastructure that vendors and developers can use for neural graphics. Branded technologies can still implement their own models and integration strategies.\"}]},\"tunes\":{}},{\"id\":\"h-glossary\",\"type\":\"header\",\"data\":{\"text\":\"Glossary\",\"level\":2},\"tunes\":{}},{\"id\":\"glossary\",\"type\":\"glossary\",\"data\":{\"title\":\"Key DirectX ML terms\",\"entries\":[{\"term\":\"DX Linear Algebra\",\"definition\":\"DirectX\u002FHLSL APIs for accelerated vector and matrix operations intended for neural rendering, ML and image-processing workloads.\",\"anchor\":\"dx-linear-algebra\"},{\"term\":\"Cooperative Vector\",\"definition\":\"An earlier DirectX approach for accelerated vector-matrix operations inside shaders that helped establish the shader-level neural-rendering path.\",\"anchor\":\"cooperative-vector\"},{\"term\":\"Shader Model 6.10\",\"definition\":\"The preview shader-model generation containing the current DirectX Linear Algebra matrix APIs.\",\"anchor\":\"shader-model-6-10\"},{\"term\":\"DirectX Compute Graph Compiler\",\"definition\":\"Microsoft's announced compiler API for optimizing and executing larger ML computation graphs as native DirectX GPU workloads.\",\"anchor\":\"compute-graph-compiler\"},{\"term\":\"Neural shader\",\"definition\":\"A practical term for a shader that performs learned inference or neural computation as part of graphics processing.\",\"anchor\":\"neural-shader\"},{\"term\":\"Shader-or-Graph Boundary\",\"definition\":\"A Figure Rocks model for deciding whether an ML workload belongs as small inline shader math or as a larger model-level computation graph.\",\"anchor\":\"shader-or-graph-boundary\"},{\"term\":\"Neural Shader Value Test\",\"definition\":\"A Figure Rocks workflow for deciding whether the traditional compute or memory cost avoided by a neural technique is worth its inference cost and quality trade-offs.\",\"anchor\":\"neural-shader-value-test\"}]},\"tunes\":{}},{\"id\":\"h-sources\",\"type\":\"header\",\"data\":{\"text\":\"Primary sources\",\"level\":2},\"tunes\":{}},{\"id\":\"src-ms-ml-era\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fevolving-directx-for-the-ml-era-on-windows\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Microsoft DirectX — Evolving DirectX for the ML Era on Windows\",\"description\":\"Official GDC 2026 architecture overview covering shader-level ML, DX Linear Algebra and the DirectX Compute Graph Compiler.\"}},\"tunes\":{}},{\"id\":\"src-ms-linalg\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fd3d12-linalg-preview\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Microsoft DirectX — D3D12 LinAlg Matrix Preview\",\"description\":\"Official April 2026 preview explaining the unified Linear Algebra APIs, matrix operations and neural-rendering motivation.\"}},\"tunes\":{}},{\"id\":\"src-ms-agility721\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fannouncing-agilitysdk-721-preview-and-more-shader-model-6-10-features\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Microsoft DirectX — Agility SDK 1.721 Preview\",\"description\":\"Official May 2026 release documenting Shader Model 6.10 Linear Algebra updates and preview hardware support.\"}},\"tunes\":{}},{\"id\":\"src-ms-gdc\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdeveloper.microsoft.com\u002Fen-us\u002Fgames\u002Farticles\u002F2026\u002F03\u002Fgdc-2026-evolving-directx-for-ml-era-on-windows\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Microsoft Game Dev — GDC 2026: Evolving DirectX for the ML Era\",\"description\":\"Official Microsoft Game Dev summary explaining shader-level and model-level ML and the role of the Compute Graph Compiler.\"}},\"tunes\":{}},{\"id\":\"src-ms-coop\",\"type\":\"linkTool\",\"data\":{\"link\":\"https:\u002F\u002Fdevblogs.microsoft.com\u002Fdirectx\u002Fcooperative-vector\u002F\",\"meta\":{\"image\":{\"url\":\"\"},\"title\":\"Microsoft DirectX — D3D12 Cooperative Vector\",\"description\":\"Official background on hardware-accelerated vector\u002Fmatrix operations and neural rendering directly from shader threads.\"}},\"tunes\":{}}],\"version\":\"2.31.6\"}",{"time":1225,"blocks":1226,"version":1729},1790405124627,[1227,1231,1236,1241,1245,1249,1253,1257,1261,1265,1280,1285,1289,1293,1297,1301,1320,1324,1328,1332,1336,1340,1344,1366,1370,1374,1378,1382,1389,1393,1397,1401,1405,1409,1434,1438,1442,1446,1450,1455,1459,1463,1467,1471,1475,1479,1483,1487,1494,1498,1518,1522,1526,1530,1534,1538,1542,1546,1550,1554,1577,1581,1585,1589,1593,1597,1601,1605,1609,1612,1616,1620,1623,1627,1631,1635,1638,1661,1665,1690,1694,1701,1708,1715,1722],{"id":541,"data":1228,"type":544,"tunes":1230},{"text":1229},"DirectX is no longer only a graphics API that sends traditional shaders to the GPU. Microsoft is adding machine-learning primitives directly to HLSL and a second path for running larger ML graphs inside the DirectX ecosystem. That changes where neural rendering can live inside future PC games.",{},{"id":547,"data":1232,"type":552,"tunes":1235},{"body":1233,"title":1234,"variant":551},"\u003Cstrong>DirectX is becoming an ML-capable graphics platform.\u003C\u002Fstrong> Small neural workloads can run directly inside shaders through DX Linear Algebra, while larger models are being targeted by the DirectX Compute Graph Compiler. The goal is to let game engines use GPU AI hardware without building a separate vendor-specific path for every technique.","Direct answer",{},{"id":555,"data":1237,"type":552,"tunes":1240},{"body":1238,"title":1239,"variant":559},"As of September 2026, \u003Cstrong>DX Linear Algebra is still a preview technology\u003C\u002Fstrong> in the Shader Model 6.10 \u002F Agility SDK preview path. Microsoft announced the DirectX Compute Graph Compiler for private preview rather than as a broadly shipping retail feature. This article explains the architecture and direction, not a claim that every current game can use it today.","Current status",{},{"id":562,"data":1242,"type":566,"tunes":1244},{"title":1243,"maxLevel":565,"minLevel":47},"Contents",{},{"id":569,"data":1246,"type":572,"tunes":1248},{"text":1247,"level":47},"Why neural rendering needs new DirectX primitives",{},{"id":575,"data":1250,"type":544,"tunes":1252},{"text":1251},"Modern GPUs already contain specialized hardware for matrix operations used by machine learning. The problem for a game developer is not only whether that hardware exists, but how to access it efficiently from a real-time graphics pipeline.",{},{"id":580,"data":1254,"type":544,"tunes":1256},{"text":1255},"Microsoft first explored this with Cooperative Vector support. In 2026, that work evolved into a broader DirectX Linear Algebra design that supports both vector-matrix and matrix-matrix operations.",{},{"id":585,"data":1258,"type":544,"tunes":1260},{"text":1259},"That matters because different neural-rendering jobs have different shapes. A tiny model evaluating material behavior per pixel is not the same workload as a large super-resolution or denoising graph.",{},{"id":590,"data":1262,"type":572,"tunes":1264},{"text":1263,"level":47},"The Two-Level DirectX ML Model",{},{"id":595,"data":1266,"type":612,"tunes":1279},{"steps":1267,"title":1278,"orientation":611},[1268,1271,1273,1276],{"label":1269,"description":1270},"1. Shader-level ML","Small neural or linear-algebra workloads execute directly from HLSL alongside traditional shader code.",{"label":602,"description":1272},"The shader can request hardware-accelerated vector and matrix operations instead of manually implementing every ML primitive.",{"label":1274,"description":1275},"3. Model-level ML","Larger neural networks are represented as complete computation graphs rather than hand-written shader fragments.",{"label":608,"description":1277},"Microsoft's planned compiler path analyzes and lowers those graphs into optimized GPU workloads integrated with D3D12 queues and command lists.","Two ways ML can enter the DirectX graphics pipeline",{},{"id":615,"data":1281,"type":552,"tunes":1284},{"body":1282,"title":1283,"variant":619},"\u003Cstrong>DX Linear Algebra:\u003C\u002Fstrong> put small ML math inside the shader.\u003Cbr>\u003Cstrong>Compute Graph Compiler:\u003C\u002Fstrong> bring a larger ML model into the engine as a graph.","The simple difference",{},{"id":622,"data":1286,"type":572,"tunes":1288},{"text":1287,"level":47},"What DX Linear Algebra actually gives a shader",{},{"id":627,"data":1290,"type":544,"tunes":1292},{"text":1291},"Traditional HLSL is built around graphics and compute operations. Neural workloads rely heavily on linear algebra: vectors, matrices, multiplication, accumulation and data layouts optimized for those operations.",{},{"id":632,"data":1294,"type":544,"tunes":1296},{"text":1295},"The Shader Model 6.10 preview adds first-class matrix APIs so developers can express those workloads more directly and let the driver map them to specialized hardware.",{},{"id":637,"data":1298,"type":544,"tunes":1300},{"text":1299},"Microsoft's April 2026 preview explicitly describes this as a unified path for neural rendering, ML and image-processing workloads rather than a graphics-only feature.",{},{"id":642,"data":1302,"type":666,"tunes":1319},{"rows":1303,"title":1313,"layout":658,"columns":1314},[1304,1307,1310],{"id":646,"label":1305,"values":1306},"Typical work",[13,13],{"id":650,"label":1308,"values":1309},"Hardware path",[13,13],{"id":654,"label":1311,"values":1312},"Developer expression",[13,13],"Traditional shader math vs ML-oriented shader math",[1315,1317],{"id":661,"label":1316},"Traditional shader focus",{"id":664,"label":1318},"ML-oriented focus",{},{"id":669,"data":1321,"type":572,"tunes":1323},{"text":1322,"level":47},"Why Cooperative Vector was only the beginning",{},{"id":674,"data":1325,"type":544,"tunes":1327},{"text":1326},"Cooperative Vector allowed shader threads to request vector-matrix work that drivers could map to specialized hardware. That was useful for highly parallel per-pixel workloads.",{},{"id":679,"data":1329,"type":544,"tunes":1331},{"text":1330},"Microsoft later concluded that many important ML workloads need more than vector-matrix operations. Super resolution, denoising, temporal reconstruction, larger image models and general inference can require matrix-matrix operations and shared work across many threads.",{},{"id":684,"data":1333,"type":544,"tunes":1335},{"text":1334},"DX Linear Algebra therefore broadens the model instead of treating Cooperative Vector as the final abstraction.",{},{"id":689,"data":1337,"type":572,"tunes":1339},{"text":1338,"level":47},"What can actually run inside a shader?",{},{"id":694,"data":1341,"type":544,"tunes":1343},{"text":1342},"The most interesting shader-level ML workloads are small enough to execute close to the graphics data they operate on.",{},{"id":699,"data":1345,"type":658,"tunes":1365},{"content":1346,"stretched":720,"withHeadings":15},[1347,1350,1353,1356,1359,1362],[1348,1349],"Workload","Why shader-level ML fits",[1351,1352],"Neural texture compression","A small network can reconstruct texture information near the point where the shader needs it",[1354,1355],"Neural material evaluation","A learned function can replace or augment expensive hand-authored material math",[1357,1358],"Neural radiance caching","Per-pixel or local inference can estimate lighting information from learned scene behavior",[1360,1361],"Small denoising\u002Freconstruction kernels","ML operations can sit directly beside the rendering stage they improve",[1363,1364],"Image-processing inference","Matrix operations can be embedded into GPU processing without a separate external runtime",{},{"id":723,"data":1367,"type":572,"tunes":1369},{"text":1368,"level":47},"Why this matters for texture memory",{},{"id":728,"data":1371,"type":544,"tunes":1373},{"text":1372},"One of Microsoft's recurring examples is neural texture compression.",{},{"id":733,"data":1375,"type":544,"tunes":1377},{"text":1376},"Instead of storing every texture channel at conventional fidelity, a game can store a more compact representation and use a small neural network to reconstruct detail during rendering.",{},{"id":738,"data":1379,"type":544,"tunes":1381},{"text":1380},"That trades some GPU inference work for lower storage or memory pressure. The exact benefit depends on the technique and hardware, but the architectural change is important: some visual detail can become computation instead of stored data.",{},{"id":743,"data":1383,"type":749,"tunes":1388},{"url":1384,"title":1385,"excerpt":1386,"ctaLabel":1387},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fvram-usage-is-not-vram-requirement-why-a-full-memory-meter-does-not-tell-the-whole-story","VRAM Usage Is Not VRAM Requirement: Why a Full Memory Meter Does Not Tell the Whole Story","A practical guide to VRAM capacity, residency budgets, working sets and why memory pressure is more complicated than one usage number.","Read the VRAM guide",{},{"id":752,"data":1390,"type":572,"tunes":1392},{"text":1391,"level":47},"Why full models need a different path",{},{"id":757,"data":1394,"type":544,"tunes":1396},{"text":1395},"Hand-writing a few matrix operations in HLSL is practical for small neural functions. It becomes much less practical when the workload is a complete modern model with many layers, dependencies and intermediate tensors.",{},{"id":762,"data":1398,"type":544,"tunes":1400},{"text":1399},"Microsoft's DirectX Compute Graph Compiler is designed for this larger class of workload.",{},{"id":767,"data":1402,"type":544,"tunes":1404},{"text":1403},"Instead of rewriting the model as custom shader code, the compiler can accept a computation graph, analyze the whole graph, plan memory, fuse operations and lower the result into GPU work that integrates with DirectX 12.",{},{"id":772,"data":1406,"type":572,"tunes":1408},{"text":1407,"level":47},"The Shader-or-Graph Boundary",{},{"id":777,"data":1410,"type":666,"tunes":1433},{"rows":1411,"title":1427,"layout":658,"columns":1428},[1412,1415,1418,1421,1424],{"id":781,"label":1413,"values":1414},"Model size",[13,13],{"id":785,"label":1416,"values":1417},"Execution style",[13,13],{"id":789,"label":1419,"values":1420},"Authoring",[13,13],{"id":793,"label":1422,"values":1423},"Optimization scope",[13,13],{"id":797,"label":1425,"values":1426},"Typical use",[13,13],"When the ML workload belongs in HLSL vs a model compiler",[1429,1431],{"id":803,"label":1430},"Shader-level path",{"id":806,"label":1432},"Model-level path",{},{"id":810,"data":1435,"type":572,"tunes":1437},{"text":1436,"level":47},"Why cross-vendor support matters more than another AI feature",{},{"id":815,"data":1439,"type":544,"tunes":1441},{"text":1440},"AMD, Intel, NVIDIA and Qualcomm expose different GPU architectures and different forms of dedicated matrix acceleration.",{},{"id":820,"data":1443,"type":544,"tunes":1445},{"text":1444},"A DirectX abstraction gives Microsoft and the driver vendors a place to translate common HLSL or graph-level ML into the correct hardware path.",{},{"id":825,"data":1447,"type":544,"tunes":1449},{"text":1448},"That does not make every GPU equally fast, but it can reduce the need for a game engine to implement a completely different neural-rendering API for every vendor.",{},{"id":830,"data":1451,"type":552,"tunes":1454},{"body":1452,"title":1453,"variant":559},"The interesting part is not that DirectX can “run AI.” GPUs already could. The platform change is that \u003Cstrong>ML becomes expressible through a common graphics API and toolchain\u003C\u002Fstrong>.","Portability is the real platform story",{},{"id":836,"data":1456,"type":572,"tunes":1458},{"text":1457,"level":47},"What hardware supports the current preview?",{},{"id":841,"data":1460,"type":544,"tunes":1462},{"text":1461},"The answer depends on the specific Linear Algebra operation and preview driver.",{},{"id":846,"data":1464,"type":544,"tunes":1466},{"text":1465},"Microsoft's Agility SDK 1.721 preview support table lists LinAlg VectorAccumulate for AMD Radeon RX 9000 Series hardware, Intel Xe2-or-newer hardware through an upcoming driver, and NVIDIA RTX hardware through the supported preview path.",{},{"id":851,"data":1468,"type":544,"tunes":1470},{"text":1469},"This is preview-era support, not a universal retail guarantee. Hardware and driver support can change before finalization.",{},{"id":856,"data":1472,"type":572,"tunes":1474},{"text":1473,"level":47},"Neural rendering is becoming infrastructure",{},{"id":861,"data":1476,"type":544,"tunes":1478},{"text":1477},"DLSS, FSR and other neural graphics technologies are often discussed as branded features visible in a game's settings menu.",{},{"id":866,"data":1480,"type":544,"tunes":1482},{"text":1481},"DirectX Linear Algebra points to a deeper change. The neural operation can become an internal implementation detail inside the renderer rather than a single optional post-processing feature.",{},{"id":871,"data":1484,"type":544,"tunes":1486},{"text":1485},"A developer could use ML for textures, materials, lighting, reconstruction or other local functions without exposing each one as a consumer-facing AI toggle.",{},{"id":876,"data":1488,"type":749,"tunes":1493},{"url":1489,"title":1490,"excerpt":1491,"ctaLabel":1492},"https:\u002F\u002Ffigure.rocks\u002Fblog\u002Fdlss-5-is-not-just-upscaling-what-3d-guided-neural-rendering-actually-changes","DLSS 5 Is Not Just Upscaling: What 3D-Guided Neural Rendering Actually Changes","How neural rendering is moving beyond upscaling and generated frames into material and lighting reconstruction inside the graphics pipeline.","Read the DLSS 5 neural-rendering guide",{},{"id":884,"data":1495,"type":572,"tunes":1497},{"text":1496,"level":47},"The Neural Rendering Stack is becoming layered",{},{"id":889,"data":1499,"type":612,"tunes":1517},{"steps":1500,"title":1516,"orientation":611},[1501,1504,1507,1510,1513],{"label":1502,"description":1503},"Traditional engine work","Simulation, geometry, visibility and base rendering remain standard engine responsibilities.",{"label":1505,"description":1506},"Inline neural shaders","Small learned functions reconstruct textures, materials or lighting inside HLSL.",{"label":1508,"description":1509},"Larger ML graphs","Full reconstruction or inference models execute through a graph-level DirectX path.",{"label":1511,"description":1512},"Vendor hardware mapping","Drivers map common DirectX operations onto the GPU's specialized AI and matrix units.",{"label":1514,"description":1515},"PIX visibility","Graphics and ML work can be profiled together instead of living in separate opaque runtimes.","A possible future DirectX game pipeline",{},{"id":910,"data":1519,"type":572,"tunes":1521},{"text":1520,"level":47},"Why unified profiling matters",{},{"id":915,"data":1523,"type":544,"tunes":1525},{"text":1524},"A neural workload that improves image quality can still damage a game if it unexpectedly consumes frame time, memory bandwidth or VRAM.",{},{"id":920,"data":1527,"type":544,"tunes":1529},{"text":1528},"Microsoft explicitly includes unified PIX visibility as part of the Compute Graph Compiler direction. That matters because developers need to see graphics and ML work in the same frame capture.",{},{"id":925,"data":1531,"type":544,"tunes":1533},{"text":1532},"If neural rendering becomes infrastructure, it must be measurable like any other rendering stage.",{},{"id":930,"data":1535,"type":572,"tunes":1537},{"text":1536,"level":47},"This does not mean every shader will become a neural network",{},{"id":935,"data":1539,"type":544,"tunes":1541},{"text":1540},"Traditional shader math remains efficient, deterministic and easy to reason about for many workloads.",{},{"id":940,"data":1543,"type":544,"tunes":1545},{"text":1544},"A neural function makes sense when it can approximate or reconstruct something expensive more efficiently, compress data, or produce quality that would otherwise require too much conventional compute or memory.",{},{"id":945,"data":1547,"type":544,"tunes":1549},{"text":1548},"The right architecture will remain hybrid.",{},{"id":950,"data":1551,"type":572,"tunes":1553},{"text":1552,"level":47},"The Neural Shader Value Test",{},{"id":955,"data":1555,"type":612,"tunes":1576},{"steps":1556,"title":1575,"orientation":611},[1557,1560,1563,1566,1569,1572],{"label":1558,"description":1559},"1. Identify the expensive traditional operation","What compute, bandwidth, memory or storage cost are you trying to replace?",{"label":1561,"description":1562},"2. Define the neural substitute","What can a small model reconstruct, predict or compress?",{"label":1564,"description":1565},"3. Measure inference cost","The ML model itself consumes GPU time, memory and bandwidth.",{"label":1567,"description":1568},"4. Measure quality stability","Look for temporal artifacts, reconstruction errors and failure cases.",{"label":1570,"description":1571},"5. Measure net savings","The technique is useful only if the avoided traditional cost is worth the added ML cost.",{"label":1573,"description":1574},"6. Test across vendors","A DirectX abstraction helps portability, but real hardware performance still differs.","When does putting ML into the rendering pipeline actually make sense?",{},{"id":979,"data":1578,"type":572,"tunes":1580},{"text":1579,"level":47},"What this means for gamers",{},{"id":984,"data":1582,"type":544,"tunes":1584},{"text":1583},"Gamers may never see a “DX Linear Algebra” switch in a graphics menu.",{},{"id":989,"data":1586,"type":544,"tunes":1588},{"text":1587},"The likely impact is indirect: smaller texture footprints, better lighting reconstruction, more efficient neural graphics, or new visual techniques that become practical because the engine can access matrix acceleration through a standard path.",{},{"id":994,"data":1590,"type":544,"tunes":1592},{"text":1591},"The feature is more important as infrastructure than as a brand.",{},{"id":999,"data":1594,"type":572,"tunes":1596},{"text":1595,"level":47},"What would change this answer?",{},{"id":1004,"data":1598,"type":544,"tunes":1600},{"text":1599},"The biggest uncertainty is the final retail shape of these APIs. DX Linear Algebra remains in preview, and the Compute Graph Compiler has not yet reached broad retail availability.",{},{"id":1009,"data":1602,"type":544,"tunes":1604},{"text":1603},"Final hardware support, API details, compiler behavior and engine adoption may change before these systems become normal shipping-game infrastructure.",{},{"id":1014,"data":1606,"type":544,"tunes":1608},{"text":1607},"If major engines adopt the abstractions directly, neural shaders could become much more common without individual game teams implementing every technique from scratch.",{},{"id":1019,"data":1610,"type":572,"tunes":1611},{"text":1021,"level":47},{},{"id":1024,"data":1613,"type":544,"tunes":1615},{"text":1614},"This article explains Microsoft's documented DirectX architecture and preview APIs. It does not claim that DX Linear Algebra currently improves performance in every game or that the Compute Graph Compiler is a finished retail product.",{},{"id":1029,"data":1617,"type":544,"tunes":1619},{"text":1618},"Microsoft's examples describe capability and intended use. Real benefits depend on the model, engine integration, GPU architecture, drivers and workload.",{},{"id":1034,"data":1621,"type":572,"tunes":1622},{"text":1036,"level":47},{},{"id":1039,"data":1624,"type":544,"tunes":1626},{"text":1625},"The important DirectX change is not another checkbox called AI.",{},{"id":1044,"data":1628,"type":544,"tunes":1630},{"text":1629},"It is that machine-learning math is moving into the graphics programming model itself. Small neural functions can sit inside shaders, larger models can move toward graph-level compilation, and the same DirectX toolchain can expose those workloads across multiple GPU vendors.",{},{"id":1049,"data":1632,"type":544,"tunes":1634},{"text":1633},"That is how neural rendering stops being one branded feature and starts becoming part of the rendering infrastructure.",{},{"id":1054,"data":1636,"type":572,"tunes":1637},{"text":1056,"level":47},{},{"id":1059,"data":1639,"type":1059,"tunes":1660},{"items":1640,"title":1659},[1641,1644,1647,1650,1653,1656],{"id":1063,"answer":1642,"question":1643},"It is a DirectX\u002FHLSL feature set for hardware-accelerated vector and matrix operations used by machine learning, neural rendering and image-processing workloads.","What is DirectX Linear Algebra?",{"id":1067,"answer":1645,"question":1646},"A useful plain-English description is a shader that includes a learned neural function or ML inference step as part of its graphics work.","What is a neural shader?",{"id":1071,"answer":1648,"question":1649},"As of September 2026 it remains in the Shader Model 6.10 \u002F Agility SDK preview path.","Is DX Linear Algebra already a normal retail DirectX feature?",{"id":1075,"answer":1651,"question":1652},"It is Microsoft's announced model-level ML compiler API intended to take larger computation graphs and lower them into optimized GPU workloads integrated with D3D12.","What is the DirectX Compute Graph Compiler?",{"id":1079,"answer":1654,"question":1655},"Small functions fit shader-level authoring well, while large models benefit from whole-graph optimization, memory planning and operator fusion.","Why not run every ML model directly in HLSL?",{"id":1083,"answer":1657,"question":1658},"Not directly. DirectX provides lower-level infrastructure that vendors and developers can use for neural graphics. Branded technologies can still implement their own models and integration strategies.","Will this replace DLSS or FSR?","DirectX Linear Algebra and neural shaders",{},{"id":1089,"data":1662,"type":572,"tunes":1664},{"text":1663,"level":47},"Glossary",{},{"id":1094,"data":1666,"type":1094,"tunes":1689},{"title":1667,"entries":1668},"Key DirectX ML terms",[1669,1672,1675,1677,1680,1683,1686],{"term":1670,"anchor":1100,"definition":1671},"DX Linear Algebra","DirectX\u002FHLSL APIs for accelerated vector and matrix operations intended for neural rendering, ML and image-processing workloads.",{"term":1673,"anchor":1104,"definition":1674},"Cooperative Vector","An earlier DirectX approach for accelerated vector-matrix operations inside shaders that helped establish the shader-level neural-rendering path.",{"term":1107,"anchor":1108,"definition":1676},"The preview shader-model generation containing the current DirectX Linear Algebra matrix APIs.",{"term":1678,"anchor":1112,"definition":1679},"DirectX Compute Graph Compiler","Microsoft's announced compiler API for optimizing and executing larger ML computation graphs as native DirectX GPU workloads.",{"term":1681,"anchor":1116,"definition":1682},"Neural shader","A practical term for a shader that performs learned inference or neural computation as part of graphics processing.",{"term":1684,"anchor":1120,"definition":1685},"Shader-or-Graph Boundary","A Figure Rocks model for deciding whether an ML workload belongs as small inline shader math or as a larger model-level computation graph.",{"term":1687,"anchor":1124,"definition":1688},"Neural Shader Value Test","A Figure Rocks workflow for deciding whether the traditional compute or memory cost avoided by a neural technique is worth its inference cost and quality trade-offs.",{},{"id":1128,"data":1691,"type":572,"tunes":1693},{"text":1692,"level":47},"Primary sources",{},{"id":1133,"data":1695,"type":1140,"tunes":1700},{"link":1135,"meta":1696},{"image":1697,"title":1698,"description":1699},{"url":13},"Microsoft DirectX — Evolving DirectX for the ML Era on Windows","Official GDC 2026 architecture overview covering shader-level ML, DX Linear Algebra and the DirectX Compute Graph Compiler.",{},{"id":1143,"data":1702,"type":1140,"tunes":1707},{"link":1145,"meta":1703},{"image":1704,"title":1705,"description":1706},{"url":13},"Microsoft DirectX — D3D12 LinAlg Matrix Preview","Official April 2026 preview explaining the unified Linear Algebra APIs, matrix operations and neural-rendering motivation.",{},{"id":1152,"data":1709,"type":1140,"tunes":1714},{"link":1154,"meta":1710},{"image":1711,"title":1712,"description":1713},{"url":13},"Microsoft DirectX — Agility SDK 1.721 Preview","Official May 2026 release documenting Shader Model 6.10 Linear Algebra updates and preview hardware support.",{},{"id":1161,"data":1716,"type":1140,"tunes":1721},{"link":1163,"meta":1717},{"image":1718,"title":1719,"description":1720},{"url":13},"Microsoft Game Dev — GDC 2026: Evolving DirectX for the ML Era","Official Microsoft Game Dev summary explaining shader-level and model-level ML and the role of the Compute Graph Compiler.",{},{"id":1170,"data":1723,"type":1140,"tunes":1728},{"link":1172,"meta":1724},{"image":1725,"title":1726,"description":1727},{"url":13},"Microsoft DirectX — D3D12 Cooperative Vector","Official background on hardware-accelerated vector\u002Fmatrix operations and neural rendering directly from shader threads.",{},"2.31.6","DirectX is moving beyond traditional graphics shaders. Microsoft is adding hardware-accelerated linear algebra directly to HLSL and a separate path for larger ML models, laying the foundation for neural textures, learned materials, neural lighting and other AI-driven rendering 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erfolgreich abgerufen",{"items":2065,"source":2140,"manualIds":2141,"manualMatchedIds":2142},[2066,2073,2079,2085,2091,2097,2103,2109,2115,2121,2127,2133],{"id":2067,"slug":2068,"title":2069,"excerpt":2070,"featuredImage":2071,"publishedAt":2072},"444","pubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning","PUBG Ally montre pourquoi les coéquipiers IA ont besoin de deux cerveaux : des réflexes rapides et un raisonnement lent","Un modèle de langage peut comprendre les tactiques et l'intention des joueurs, mais il ne devrait pas contrôler directement chaque mouvement et chaque réaction de combat. PUBG Ally présente une architecture plus pratique : un contrôle rapide par arbre de comportement pour les actions réflexes, combiné à un petit modèle de langage pour la planification, la coordination et la conversation naturelle.","\u002Fuploads\u002F2026\u002F09\u002Fpubg-ally-shows-why-ai-teammates-need-two-brains-fast-reflexes-and-slow-reasoning-1790376777825-bi2zzb.webp","2026-09-25T16:51:00.000Z",{"id":2074,"slug":2075,"title":2076,"excerpt":2077,"featuredImage":14,"publishedAt":2078},"414","digby","Digby","Parmi les premières figurines amiibo Animal Crossing, Digby occupe une position un peu plus discrète. La figurine représente l'assistant poli connu pour son travail au bureau de l'administration municipale de la série. Lorsqu'il est scanné, l'amiibo ne modifie pas radicalement un jeu. Au lieu de cela, il débloque de petites interactions, des scènes supplémentaires ou des apparitions de personnages qui relient différents titres Animal Crossing. Sa valeur est subtile. Il prolonge la présence d'un personnage familier à travers plusieurs jeux Nintendo.","2026-03-06T07:27:00.000Z",{"id":2080,"slug":2081,"title":2082,"excerpt":2083,"featuredImage":14,"publishedAt":2084},"422","timmy-tommy","Timmy & Tommy","L'amiibo Méli & Mélo appartient à la gamme de figurines amiibo Animal Crossing sortie lors de la première vague de la série. Comme les autres figurines de cette collection, il contient une petite puce NFC qui relie la figurine physique aux jeux Nintendo compatibles. Scanner la figurine ne modifie pas radicalement le gameplay, mais cela offre systématiquement des interactions liées aux personnages. La valeur de cet amiibo réside principalement dans sa capacité à invoquer les jumeaux commerçants dans les titres compatibles et à débloquer de petits éléments thématiques qui leur sont liés.","2026-03-06T16:48:00.000Z",{"id":2086,"slug":2087,"title":2088,"excerpt":2089,"featuredImage":14,"publishedAt":2090},"424","rover","Rover","L'amiibo Charly occupe une place familière dans la gamme Animal Crossing. Ce n'est pas une figurine qui change tout un jeu à elle seule. Son utilité est plus limitée que cela. Il permet à Charly d'apparaître là où Nintendo a autorisé la prise en charge des amiibo, et c'est vraiment là tout son intérêt. La valeur provient de l'accès, de la reconnaissance et d'un lien direct avec l'un des visages les plus anciens de la série.","2026-03-06T17:33:00.000Z",{"id":2092,"slug":2093,"title":2094,"excerpt":2095,"featuredImage":14,"publishedAt":2096},"127","motion-clarity-why-blur-happens-and-the-fixes-that-actually-work","Clarté du mouvement : pourquoi le flou se produit et les solutions qui fonctionnent vraiment","Le flou de mouvement n'est pas seulement un réglage — c'est le timing plus la persistance. Apprenez l'ordre pratique : le bon mode, le bon rafraîchissement, puis les quelques correctifs de netteté qui comptent.","2026-02-20T11:00:00.000Z",{"id":2098,"slug":2099,"title":2100,"excerpt":2101,"featuredImage":14,"publishedAt":2102},"393","isabelle-winter-outfit","Isabelle - Winter Outfit","L'amiibo Marie – Tenue d'hiver de la collection Super Smash Bros. représente une version saisonnière de l'un des personnages de soutien les plus reconnaissables de Nintendo. Cette figurine n'introduit pas de nouveau personnage, mais elle présente sous un nouveau jour un personnage déjà établi. La valeur ajoutée réside principalement dans sa compatibilité fonctionnelle avec plusieurs consoles Nintendo et dans son interprétation physique de Marie lors d'un moment saisonnier spécifique dans l'univers d'Animal Crossing.","2026-02-23T01:00:00.000Z",{"id":2104,"slug":2105,"title":2106,"excerpt":2107,"featuredImage":14,"publishedAt":2108},"421","kicks","Kicks","L'amiibo Blaise appartient à la gamme de figurines amiibo Animal Crossing sortie lors du début de l'expansion de l'écosystème de figurines NFC de Nintendo. Comme les autres personnages de cette série, la figurine fonctionne comme une clé physique qui se connecte aux jeux Nintendo compatibles via le NFC. Lorsqu'il est scanné, l'amiibo lie le personnage Blaise à différents systèmes en jeu. Sa valeur pratique est simple : il permet aux joueurs d'accéder à des interactions spécifiques au personnage, de petits déblocages ou du contenu thématique selon le titre pris en charge.","2026-03-06T15:31:00.000Z",{"id":2110,"slug":2111,"title":2112,"excerpt":2113,"featuredImage":14,"publishedAt":2114},"399","beyond-scanning-creative-uses-and-modifications-of-amiibo","Au-delà du scan – Utilisations créatives et modifications des amiibo\"","Les amiibo sont des figurines de personnages équipées de la technologie NFC commercialisées par Nintendo depuis 2014. Techniquement, ce sont de petits supports de données à l'intérieur de figurines en plastique moulé. En pratique, ils se situent quelque part entre le jouet, l'objet de collection et le dispositif d'interface. Les scanner dans une console n'est qu'une partie de leur cycle de vie. Au fil des ans, on a pu observer que de nombreux propriétaires les traitent comme des objets dotés de leur propre potentiel créatif.","2026-02-27T16:36:00.000Z",{"id":2116,"slug":2117,"title":2118,"excerpt":2119,"featuredImage":14,"publishedAt":2120},"419","celeste","Celeste","L'amiibo Céleste appartient à la gamme de figurines amiibo Animal Crossing sortie lors de la première vague de figurines dédiées à l'univers d'Animal Crossing. Comme les autres figurines de cette collection, il fonctionne comme un petit support NFC connecté à l'écosystème amiibo de Nintendo. Lorsqu'elle est scannée, la figurine lie le personnage de Céleste aux jeux compatibles. La valeur de l'amiibo réside principalement dans le fait de permettre des apparitions du personnage et de petites interactions de jeu qui n'apparaissent autrement que dans des circonstances spécifiques.","2026-03-06T14:42:00.000Z",{"id":2122,"slug":2123,"title":2124,"excerpt":2125,"featuredImage":14,"publishedAt":2126},"395","mabel","Mabel","L'amiibo Layette appartient à la gamme de figurines amiibo Animal Crossing. Elle représente la hérissonne couturière liée à la boutique de vêtements qui apparaît tout au long de la série. La figurine n'introduit pas de nouveau personnage. Elle transfère un rôle de boutique établi dans un format scannable pour les consoles Nintendo compatibles.","2026-02-23T01:58:00.000Z",{"id":2128,"slug":2129,"title":2130,"excerpt":2131,"featuredImage":14,"publishedAt":2132},"415","reese","Reese","L'amiibo de Risette appartient à la série de figurines amiibo Nintendo Animal Crossing et représente l'un des commerçants de l'économie de la ville dans les jeux Animal Crossing. Comme pour les autres figurines de cette gamme, la valeur réside moins dans l'objet en plastique lui-même que dans la puce NFC située à l'intérieur du socle. Lorsqu'elle est scannée avec des consoles Nintendo compatibles, la figurine déclenche de petites interactions en jeu, débloque des apparences de personnages ou permet d'accéder à des dialogues et des objets supplémentaires selon le titre.","2026-03-06T09:00:00.000Z",{"id":2134,"slug":2135,"title":2136,"excerpt":2137,"featuredImage":2138,"publishedAt":2139},"451","windows-auto-sr-is-not-dlss-how-npu-upscaling-works-without-game-integration","Windows Auto SR n'est pas le DLSS : comment fonctionne la mise à l'échelle par NPU sans intégration au jeu","Windows Auto SR peut mettre à l'échelle les jeux pris en charge sans intégration de DLSS, FSR ou XeSS. Au lieu d'exécuter le modèle de reconstruction dans le jeu sur le GPU, Windows utilise le NPU pour reconstruire une image en plus haute résolution à partir d'un rendu en plus basse résolution.","\u002Fuploads\u002F2026\u002F09\u002Fwindows-auto-sr-is-not-dlss-how-npu-upscaling-works-without-game-integration-1790406942266-77ihme.webp","2026-09-26T03:14:00.000Z","fallback",[],[]]