Deep Learning using Approximate Hardware

Title: Deep Learning using Approximate Hardware

Abstract: We demo deep learning algorithms doing real-time vision on an "approximate computer". The machine is a prototype for embedded applications that provides 10x the compute per watt of a modern GPU. The technology allows a single (non-prototype) chip to contain 256,000 cores and to compute using 50x less energy that an equivalent GPU. A rack could hold 50 million cores, and accelerate deep learning training and other applications. The technology has been funded by DARPA (U.S.) and tested at MIT CSAIL, Carnegie Mellon, and elsewhere.