An Approximate Backpropagation Learning Rule for Memristor Based Neural Networks Using Synaptic Plasticity

2015/11/22 by D. V. Negrov, Iakov Karandashev, Negrov, D. V. +9 · 1 citation
Computer Science · Engineering · #Advanced Memory and Neural Computing #CCD and CMOS Imaging Sensors #Emerging Technologies (cs.ET) #FOS: Computer and information sciences #Neural Networks and Applications #Neural and Evolutionary Computing (cs.NE)

paper · pdf · doi:10.48550/arxiv.1511.07076

openalex publication_date 2015/11/22 · openalex created_date 2016/08/23 · openalex updated_date 2026/07/28

Abstract

We describe an approximation to backpropagation algorithm for training deep neural networks, which is designed to work with synapses implemented with memristors. The key idea is to represent the values of both the input signal and the backpropagated delta value with a series of pulses that trigger multiple positive or negative updates of the synaptic weight, and to use the min operation instead of the product of the two signals. In computational simulations, we show that the proposed approximation to backpropagation is well converged and may be suitable for memristor implementations of multilayer neural networks.

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• Magnetic-field controlled organic spintronic memristor for neural network computation

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