Phil Brown — How IPUs are Advancing Machine Intelligence

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, intro1:44 From computational chemistry to Graphcore5:16 The simulations behind weather prediction10:54 Measuring improvement in weather prediction systems15:35 How high performance computing and ML have different needs19:00 The potential of sparse training31:08 IPUs and computer architecture for machine learning39:10 On performance improvements44:43 The impacts of increasing computing capability50:24 The ML chicken and egg problem52:00 The challenges of converging at scale and bringing hardware to marketLinks Discussed:Rigging the Lottery: Making All Tickets Winners (Evci et al., 2019): https://arxiv.org/abs/1911.11134Graphcore MK2 Benchmarks: https://www.graphcore.ai/mk2-benchmarksCheck 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