Ontology highlight
ABSTRACT:
SUBMITTER: Joshi V
PROVIDER: S-EPMC7235046 | biostudies-literature | 2020 May
REPOSITORIES: biostudies-literature
Nature communications 20200518 1
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 appreciab ...[more]