Phil shares some of the approaches, like sparsity and low precision, behind the breakthrough performance of Graphcore's Intelligence Processing Units (IPUs).
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:
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
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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