How to build a processor for machine learning

Graphcore CTO, Simon Knowles, talks about hardware for machine intelligence with Project Juno’s Libby Kinsey, after presenting at their inaugural Machine Intelligence Showcase in London. Here’s an extract of their conversation, which you can find on Libby’s blog. Compute, data, and algorithms have combined to power the recent huge strides in machine intelligence. But there is still plenty of scope for improvement, and hardware is finally coming to the fore.  We’ve heard that it is prohibitively expensive for startups and academics to train machine learning models, and this is due to the rental or purchase costs of hardware. The results from one recent Google paper were estimated to cost $13k to emulate: That’s just to reproduce the final model, not to emulate the whole experimentation and hyperparameter optimisation caboodle. Equally,…


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