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

Check out the transcription and discover more awesome ML projects: http://wandb.me/gd-phil-brown

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