A Multilayer Neural Accelerator With Binary Activations Based on Phase-Change Memory

Abstract

Novel in-memory computing circuits, based on arrays of emerging nonvolatile memories, such as the phase-change memory (PCM), can boost cutting-edge performances of artificial intelligent applications. However, the spread of PCM-based circuits is currently hindered by the lack of a design framework enabling fast, efficient, and low-power neural networks. In this work, a novel approach to the conceptual and technical design of integrated neural networks is proposed. In particular, to relax the power hunger and complexity of state-of-the-art solutions, we propose a fully analog computing approach where the analog-to-digital converter (ADC) is replaced by a simple comparator. The analog building blocks of the accelerator are presented and validated in Cadence Virtuoso. The major nonidealities, such as PCM conductance variability, conductance drift, IR drop, and readout threshold, are studied by considering their impact on accuracy.