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The past decade has seen a remarkable series of advances in machine learning,and in particular deep learning approaches based on artificial neural networks,to improve our abilities to build more accurate systems across a broad range ofareas, including computer vision, speech recognition, language translation, andnatural language understanding tasks. This paper is a companion paper to akeynote talk at the 2020 International Solid-State Circuits Conference (ISSCC)discussing some of the advances in machine learning, and their implications onthe kinds of computational devices we need to build, especially in thepost-Moore's Law-era. It also discusses some of the ways that machine learningmay also be able to help with some aspects of the circuit design process.Finally, it provides a sketch of at least one interesting direction towardsmuch larger-scale multi-task models that are sparsely activated and employ muchmore dynamic, example- and task-based routing than the machine learning modelsof today.

The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design