Multinary Data Processing Based on Nonlinear Synaptic Devices

Abstract

Stasis weight ( $$\hbox {G}_s$$ ) that is an independent weight on applied input pulse amplitude was demonstrated for multinary data processing in a synaptic device-based neuromorphic system. Because multinary data is implemented as various input pulse amplitudes, the $$\hbox {G}_s$$ is necessary for high inference accuracy. Typically, synaptic devices exhibit nonlinear current–voltage characteristics (nonlinear device) and have different weight values depending on the applied input pulse amplitudes ( $$\hbox {G}_{{\mathrm{ns}}}$$ ). Therefore, to achieve high inference accuracy, we proposed pulse modulation circuits that can transform the pulse amplitude into pulse width or number. As a result, the $$\hbox {G}_s$$ was obtained from the nonlinear device possessing the $$\hbox {G}_{{\mathrm{ns}}}$$ , and the inference accuracy of the simulated MNIST data set was obviously improved from 29.34% to 97.6%.