SC: Hardware approaches to machine learning and inference

Abstract

Advances in artificial intelligence are already changing how computing systems interact with users and interact with their environments, with further dramatic changes on the horizon. In this context, machine learning and inference operations have become a critically important computational workload, and the importance of this workload will continue to increase. Today, GPU-, CPU-, and FPGA-based engines dominate the compute landscape for learning and for inference, but the exploration of alternative, enhanced, or complementary compute capability in this problem space is an active and growing research area. In this short course, we will provide a framework for understanding some of the computational challenges in machine learning and inference and discuss emerging technical approaches aimed at meeting those challenges.