Multinary Data Processing Based on Nonlinear Synaptic Devices

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%.