57 min
Phil Brown — How IPUs are Advancing Machine Intelligence Gradient Dissent: Conversations on AI
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- Technology
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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Get our podcast on these platforms:
Apple Podcasts: http://wandb.me/apple-podcasts
Spotify: http://wandb.me/spotify
Google Podcasts: http://wandb.me/google-podcasts
YouTube: http://wandb.me/youtube
Soundcloud: http://wandb.me/soundcloud
Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack
Check out our Gallery, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/gallery
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:
LinkedIn: https://www.linkedin.com/in/philipsbrown/
Twitter: https://twitter.com/phil_s_brown
---
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
---
Get our podcast on these platforms:
Apple Podcasts: http://wandb.me/apple-podcasts
Spotify: http://wandb.me/spotify
Google Podcasts: http://wandb.me/google-podcasts
YouTube: http://wandb.me/youtube
Soundcloud: http://wandb.me/soundcloud
Join our community of ML practitioners where we host AMAs, share interesting projects and meet other people working in Deep Learning: http://wandb.me/slack
Check out our Gallery, which features curated machine learning reports by researchers exploring deep learning techniques, Kagglers showcasing winning models, industry leaders sharing best practices, and more: https://wandb.ai/gallery
57 min