MLC PCM Techniques to Improve Nerual Network Inference Retention Time by 105X and Reduce Accuracy Degradation by 10.8X

We present three novel MLC PCM techniques – (1) device requirement balancing, (2) prediction-based MSB-biased referencing, and (3) bit-prioritized placement to address the MLC device challenges in neural network applications. Using measured MLC bit error rates, the proposed techniques can improve the MLC PCM retention time by 10
⁵
times while keeping the ResNet-20 inference accuracy degradation within 3% and reduce the accuracy degradation by 91% (10.8X) for CIFAR-100 dataset in the presence of temporal resistance drift.