For the last decade, advances in machine learning have come from two things: improved compute power and better algorithms. These two areas have become somewhat siloed in most people’s thinking: we tend to imagine that there are people who build hardware, and people who make algorithms, and that there isn’t much overlap between the two.

But this picture is wrong. Hardware constraints can and do inform algorithm design, and algorithms can be used to optimize hardware. Increasingly, compute and modelling are being optimized together, by people with expertise in both areas.

My guest today is one of the world’s leading experts on hardware/software integration for machine learning applications. Max Welling is a former physicist and currently works as VP Technologies at Qualcomm, a world-leading chip manufacturer, in addition to which he’s also a machine learning researcher with affiliations at UC Irvine, CIFAR and the University of Amsterdam.

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