Affiliation:
1. Department of Physics, School of Applied Mathematical and Physical Sciences, National Technical University of Athens , Iroon Polytechniou 9, 15780 Zografou, Greece
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.
Funder
Hellenic Foundation for Research and Innovation
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献