Impact of Asymmetric Weight Update on Neural Network Training With Tiki-Taka Algorithm.

ABSTRACT: Recent progress in novel non-volatile memory-based synaptic device technologies and their feasibility for matrix-vector multiplication (MVM) has ignited active research on implementing analog neural network training accelerators with resistive crosspoint arrays. While significant performance boost as well as area- and power-efficiency is theoretically predicted, the realization of such analog accelerators is largely limited by non-ideal switching characteristics of crosspoint elements. One of the most performance-limiting non-idealities is the conductance update asymmetry which is known to distort the actual weight change values away from the calculation by error back-propagation and, therefore, significantly deteriorates the neural network training performance. To address this issue by an

SUBMITTER: Lee C 

PROVIDER: S-EPMC8770851 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature