A Read Disturbance Tolerant Phase Change Memory System for CNN Inference Workloads

Abstract

Phase-change memory (PCM) garners attention as the most promising nonvolatile memory (NVM). In particular, PCM is suitable for applications that are not memory intensive, and the convolutional neural network (CNN) inference is widely known as a representative computation-intensive model. Therefore, CNN inference seems to be very suitable for a PCM-based system. However, the PCM suffers from the characteristic of being vulnerable to disturbance errors. In particular, read disturbance error (RDE) becomes a serious problem for workloads involving a large number of zeros, and unfortunately, matrices in CNN are sparse, which inevitably incurs a significant amount of RDEs. In this paper, we present an RDE-tolerant PCM-based system for CNN inference workloads. The proposed method restores vulnerable data words by leveraging a dedicated SRAM-based table. Furthermore, we also propose a replacement policy, which detects non-urgent entries, by utilizing the contents (i.e., counters) in the table. As a result, the proposed method significantly reduces RDEs with minor speed degradation.