Conference Paper


Learning method for ex-situ training of memristor crossbar based multi-layer neural network

Abstract

Memristor is being considered as a game changer for the realization of neuromorphic hardware systems due to its similarity with biological synapse. Recent studies show that memristor crossbar can provide high density and high performance neural network hardware implementation at low power due to its physical layout, nano scale size and low power consumption feature. This paper describes the training method that can be used for the implementation of memristive multi-layer neural network with ex-situ method. We mimic the behavior of memristor crossbar in software training process to achieve more accurate and close computations to hardware. Voltage divider has been used to calculate the dot product in this method. To demonstrate the accuracy and effectiveness of this method, different patterns and non-separable functions using memristor crossbar structures are simulated. The results demonstrate that more accurate computations can be produced using this learning method for ex-situ. It also reduces the learning time of functions.

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Authors

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

Oxford Brookes departments

Faculty of Technology, Design and Environment\Department of Computing and Communication Technologies

Dates

Year of publication: 2018
Date of RADAR deposit: 2017-10-31



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This RADAR resource is the Accepted Manuscript of Learning method for ex-situ training of memristor crossbar based multi-layer neural network

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