AI hardware acceleration with analog memory: Microarchitectures for low energy at high speed

Abstract

In this article, we present innovative microarchitectural designs for multilayer deep neural networks (DNNs) implemented in crossbar arrays of analog memories. Data is transferred in a fully parallel manner between arrays without explicit analog-to-digital converters. Design ideas including source follower-based readout, array segmentation, and transmit-by-duration are adopted to improve the circuit efficiency. The execution energy and throughput, for both DNN training and inference, are analyzed quantitatively using circuit simulations of a full CMOS design in the 90-nm technology node. We find that our current design could achieve up to 12-14 TOPs/s/W energy efficiency for training, while a projected scaled design could achieve up to 250 TOPs/s/W. Key challenges in realizing analog AI systems are discussed.