R&D: Inference of Long-Short Term Memory Networks at Software-Equivalent Accuracy Using 2.5M Analog Phase Change Memory Devices

IEEE Xplore has published, in 2019 Symposium on VLSI Technology proceedings, an article written by H. Tsai, S. Ambrogio, C. Mackin, P. Narayanan, R. M. Shelby, K. Rocki, A. Chen, and G. W. Burr, IBM Research-Almaden, 650 Harry Road, San Jose, CA, 95120.

Abstract: “We report accuracy for forward inference of long-short-term-memory (LSTM) networks using weights programmed into the conductances of >2.5M phase-change memory (PCM) devices. We demonstrate strategies for software weight-mapping and programming of hardware analog conductances that provide accurate weight programming despite significant device variability. Inference accuracy very close to software-model baselines is achieved on several language modeling tasks.“