  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 of
areas, including computer vision, speech recognition, language translation, and
natural language understanding tasks. This paper is a companion paper to a
keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC)
discussing some of the advances in machine learning, and their implications on
the kinds of computational devices we need to build, especially in the
post-Moore's Law-era. It also discusses some of the ways that machine learning
may also be able to help with some aspects of the circuit design process.
Finally, it provides a sketch of at least one interesting direction towards
much larger-scale multi-task models that are sparsely activated and employ much
more dynamic, example- and task-based routing than the machine learning models
of today.
