Training complex machine learning models with large amounts of data can take a very long time. Thomas Norrie explores methods for accelerating this process by distributing training across multiple accelerators and machines and leads a technical deep dive into Google Cloud’s TPU accelerators.
This session is sponsored by Google.
Thomas Norrie is hardware engineer on Google’s Platforms team, where he’s helped develop multiple generations of TPUs, the deep learning hardware accelerator. He finds the intersection of hardware, software, and systems to be both fascinating and productive for solving big problems. While he’s wistful for the good ol’ days of unabated Moore’s law, he’s also excited about the opportunities to think more comprehensively about domain-specific computing.
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