Pulse-stream impact on recognition accuracy of reservoir computing from SiO2-based low power memory devices

Abstract

Reservoir computing (RC)-based neuromorphic applications exhibit extremely low power consumption, thus challenging the use of deep neural networks in terms of both consumption requirements and integration density. Under this perspective, this work focuses on the basic principles of RC systems. The ability of self-selective conductive-bridging random access memory devices to operate in two modes, namely, volatile and non-volatile, by regulating the applied voltage is first presented. We then investigate the relaxation time of these devices as a function of the applied amplitude and pulse duration, a critical step in determining the desired non-linearity by the reservoir. Moreover, we present an in-depth study of the impact of selecting the appropriate pulse-stream and its final effects on the total power consumption and recognition accuracy in a handwritten digit recognition application from the National Institute of Standards and Technology dataset. Finally, we conclude at the optimal pulse-stream of 3-bit, through the minimization of two cost criteria, with the total power remaining at 287 µW and simultaneously achieving 82.58% recognition accuracy upon the test set.