S. R. Nandakumar, Manuel Le Gallo, et al.
ISCAS 2018
In-memory computing using resistive memory devices is a promising non-von Neumann approach for making energy-efficient deep learning inference hardware. However, due to device variability and noise, the network needs to be trained in a specific way so that transferring the digitally trained weights to the analog resistive memory devices will not result in significant loss of accuracy. Here, we introduce a methodology to train ResNet-type convolutional neural networks that results in no appreciable accuracy loss when transferring weights to phase-change memory (PCM) devices. We also propose a compensation technique that exploits the batch normalization parameters to improve the accuracy retention over time. We achieve a classification accuracy of 93.7% on CIFAR-10 and a top-1 accuracy of 71.6% on ImageNet benchmarks after mapping the trained weights to PCM. Our hardware results on CIFAR-10 with ResNet-32 demonstrate an accuracy above 93.5% retained over a one-day period, where each of the 361,722 synaptic weights is programmed on just two PCM devices organized in a differential configuration.
S. R. Nandakumar, Manuel Le Gallo, et al.
ISCAS 2018
Corey Liam Lammie, Manuel Le Gallo, et al.
ISCAS 2024
Geethan Karunaratne, Manuel Le Gallo, et al.
Nature Electronics
Manuel Le Gallo, Abu Sebastian
Journal of Physics D: Applied Physics