Machine Learning Prediction for 13X Endurance Enhancement in ReRAM SSD System

Abstract

The variable behavior of ReRAM memory cells is modeled with machine learning. Two types of prediction are investigated, reset in the next-cycle and cell fail in the long term. A new proposal, Proactive Bit Redundancy, introduces a ML-trained Prediction Engine into the SSD controller, to predict fail cells and replace them proactively - before actual failure- by redundancy. With the Invalid Masking technique, predicted cells are marked in-place within the page, so that no extra address table is needed. Thus, with ninimal overhead, 2.85x bit error rate reduction or 13x endurance improvement is obtained based on a 50nm AlxOy testchip.