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