Accelerating Deep Neural Networks with Analog Memory Devices

Abstract

Analog memory offers enormous potential to speed up computation in deep learning. We study the use of phase-change memory (PCM) as the resistive element in a crossbar array that allows the multiply-accumulate operation in deep neural networks to be performed in-memory. With this promise comes several challenges, including this paper’s main focus: the impact of conductance drift on deep neural network accuracy. Here we offer an overview of our recent work, including explanations of popular neural network architectures, along with a technique to compensate for drift ("slope correction") to allow in-memory computing with PCM during inference to reach software-equivalent deep learning baselines for a broad variety of important neural network workloads.