Mixed-Precision Deep Learning Based on Computational Memory.

ABSTRACT: Deep neural networks (DNNs) have revolutionized the field of artificial intelligence and have achieved unprecedented success in cognitive tasks such as image and speech recognition. Training of large DNNs, however, is computationally intensive and this has motivated the search for novel computing architectures targeting this application. A computational memory unit with nanoscale resistive memory devices organized in crossbar arrays could store the synaptic weights in their conductance states and perform the expensive weighted summations in place in a non-von Neumann manner. However, updating the conductance states in a reliable manner during the weight update process is a fundamental challenge that limits the training accuracy of such an implementation. Here, we propose a mixed-precision

SUBMITTER: Nandakumar SR 

PROVIDER: S-EPMC7235420 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature