Weights & Biases
Phil shares some of the approaches, like sparsity and low precision, behind the breakthrough performance of Graphcore’s Intelligence Processing Units (IPUs).
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Phil Brown leads the Applications team at Graphcore, where they’re building high-performance machine learning applications for their Intelligence Processing Units (IPUs), new processors specifically designed for AI compute.
Connect with Phil:
LinkedIn: https://www.linkedin.com/in/philipsbrown/
Twitter: https://twitter.com/phil_s_brown
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0:00 Sneak peek, intro
1:44 From computational chemistry to Graphcore
5:16 The simulations behind weather prediction
10:54 Measuring improvement in weather prediction systems
15:35 How high performance computing and ML have different needs
19:00 The potential of sparse training
31:08 IPUs and computer architecture for machine learning
39:10 On performance improvements
44:43 The impacts of increasing computing capability
50:24 The ML chicken and egg problem
52:00 The challenges of converging at scale and bringing hardware to market
Links Discussed:
Rigging the Lottery: Making All Tickets Winners (Evci et al., 2019): https://arxiv.org/abs/1911.11134
Graphcore MK2 Benchmarks: https://www.graphcore.ai/mk2-benchmarks
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