Multinary Data Processing Based on Nonlinear Synaptic Devices

Stasis weight ( 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 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 ( G_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 G_s was obtained from the nonlinear device possessing the G_ns , and the inference accuracy of the simulated MNIST data set was obviously improved from 29.34