What is recorded here?
Exposed control/dataflow engines, specialized local memories and synchronization to the software stack, connecting Maia100's hardware/software design to Maia200's inference-focused memory/network hierarchy.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
Bundles two original technical accounts with separate bylines;2024 is the first cited technical presentation, not a claim of first announcement. SDLA categorization acknowledges existing related architectures rather than inventing all dataflow. Peak FP4/FP8 rates and internal TCO/energy claims are not independent end-to-end certification. Programmer/compiler obligations and workload/network/memory conditions remain; future Maia300 deployment plans are not counted.
Read the original work
- Inside Maia100 (Hot Chips2024 original technical presentation)
- Maia 200: A Software Defined Dataflow System for Large-scale AI Acceleration
This English explanation is an editorial reading view. It does not replace the complete original work or certify its science.
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People and systems
Sherry Xu; Chandru Ramakrishnan; Marco Heddes; Jackson Peng; Tom Savell; Monica Tang; Prashant Ranjan; Jesse Benson; Ofer Dekel; Saurabh Dighe; Anupama Kurpad; Artour Levin; Matthew Mattina; George Petre; Cheng Tang; Yuan Yu; Li Zhang; Torsten Hoefler
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
Research locations and affiliations
Location not yet established
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English catalogue narrative
Microsoft Maia: software-defined dataflow and accelerator systems co-design
Exposed control/dataflow engines, specialized local memories and synchronization to the software stack, connecting Maia100's hardware/software design to Maia200's inference-focused memory/network hierarchy.
Scientific scope
Bundles two original technical accounts with separate bylines;2024 is the first cited technical presentation, not a claim of first announcement. SDLA categorization acknowledges existing related architectures rather than inventing all dataflow. Peak FP4/FP8 rates and internal TCO/energy claims are not independent end-to-end certification. Programmer/compiler obligations and workload/network/memory conditions remain; future Maia300 deployment plans are not counted.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Sherry Xu; Chandru Ramakrishnan; Marco Heddes; Jackson Peng; Tom Savell; Monica Tang; Prashant Ranjan; Jesse Benson; Ofer Dekel; Saurabh Dighe; Anupama Kurpad; Artour Levin; Matthew Mattina; George Petre; Cheng Tang; Yuan Yu; Li Zhang; Torsten Hoefler
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.