Efficient and low overhead memristive activation circuit for deep learning neural networks

Bala, Anu; Yang, Xiaohan; Adeyemo, Adedotun; Jabir, Abusaleh

Journal Article

Efficient and low overhead memristive activation circuit for deep learning neural networks

Abstract

An efficient memristor MIN function based activation circuit is presented for memristive neuromorphic systems, using only two memristors and a comparator. The ReLU activation function is approximated using this circuit. The ReLU activation function helps to significantly reduce the time and computational cost of training in neuromorphic systems due to its simplicity and effectiveness in deep neural networks. A multilayer neural network is simulated using this activation circuit in addition to traditional memristor crossbar arrays. The results illustrate that the proposed circuit is able to perform training effectively with significant savings in time and area in memristor crossbar based neural networks.