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October 22-23, 2024
Santa Clara, CA
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Tuesday October 22, 2024 11:50am - 12:28pm PDT
There has been huge interest in building accelerators for AI in the decade since AlexNet ushered in the current deep learning revolution. Billions of dollars in capital have been committed, and many ambitious projects have been launched, across established manufacturers, hyperscalers, and startups. In this talk, we will reflect on our experiences at Google designing and deploying successful accelerators and the different ways that subtle challenges make effective acceleration hard. RISC-V potentially helps with these challenges, while lowering barriers to entry, reducing risks, and sharing the benefit of expertise and experience. We will make connections between our experiences and how RISC-V accelerates accelerator development itself, highlighting how the shared work on a RISC-V ecosystem for deep learning acceleration can be positive-sum, benefiting all who participate.
Speakers
avatar for Martin Maas

Martin Maas

Staff Research Scientist, Google DeepMind
Martin Maas is a Staff Research Scientist at Google DeepMind. His research interests are in language runtimes, computer architecture, systems, and machine learning, with a focus on applying ML to systems problems. He also chairs the RISC-V J Extension Task Group, which investigates... Read More →
avatar for Cliff Young

Cliff Young

Software Engineer, Google DeepMind
Cliff Young is a software engineer in Google DeepMind, where he works on codesign for deep learning accelerators. He is one of the designers of Google’s Tensor Processing Unit (TPU) and one of the founders of the MLPerf benchmark. Previously, Cliff built special-purpose supercomputers... Read More →
Tuesday October 22, 2024 11:50am - 12:28pm PDT
Grand Ballroom G (Level 1)
  AI / ML

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