翻訳待ち：Maia 200: A Software Defined Dataflow System for Large-Scale AI Acceleration

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

--> [Submitted on 25 Aug 2026] Title:Maia 200: A Software Defined Dataflow System for Large-scale AI Acceleration View a PDF of the paper titled Maia 200: A Software Defined Dataflow System for Large-scale AI Acceleration, by Sherry Xu and 16 other authors View PDF HTML (experimental) Abstract:We introduce Maia 200, an advanced AI accelerator delivering high performance-10 145 Tflop/s FP4 and 5072 Tflop/s FP8 within a 750W TDP and 7 TB/s HBM bandwidth. Maia exemplifies a new class of Software Defined Locally Accessed Dataflow Architectures (SDLA), which explicitly program dataflow engines to orchestrate highly specialized memories and data movement engines. This approach shifts the focus from today's thread-centric to data-movement-centric architecture, improving efficiency and scalability. Our taxonomy of data management, inspired by Flynn's classification, highlights how SDLA addresses challenges in modern AI computing. Maia 200 achieves significant cost and energy savings while supporting massive parallelism for AI inference workloads, making it a compelling solution for next-generation high-performance computing systems. Subjects: Hardware Architecture (cs.AR); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Emerging Technologies (cs.ET); Machine Learning (cs.LG) Cite as: arXiv:2608.24664 [cs.AR] (or arXiv:2608.24664v1 [cs.AR] for this version) https://doi.org/10.48550/arXiv.2608.24664 arXiv-issued DOI via DataCite (pending registration) Submission history From: Torsten Hoefler [view email] [v1] Tue, 25 Aug 2026 15:05:40 UTC (1,145 KB) Full-text links: Access Paper: View a PDF of the paper titled Maia 200: A Software Defined Dataflow System for Large-scale AI Acceleration, by Sherry Xu and 16 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AR new | recent | 2026-08 Change to browse by: cs cs.AI cs.DC cs.ET cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Loading... Data provided by: Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)