Efficient Mini-Batch Training on Memristor Neural Network Integrating Gradient Calculation and Weight Update

Satoshi YAMAMORI  Masayuki HIROMOTO  Takashi SATO  

IEICE TRANSACTIONS on Fundamentals of Electronics, Communications and Computer Sciences   Vol.E101-A   No.7   pp.1092-1100
Publication Date: 2018/07/01
Online ISSN: 1745-1337
DOI: 10.1587/transfun.E101.A.1092
Type of Manuscript: PAPER
Category: Neural Networks and Bioengineering
memoristor,  neural network,  mini-batch training,  stochastic gradient descent,  

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We propose an efficient training method for memristor neural networks. The proposed method is suitable for the mini-batch-based training, which is a common technique for various neural networks. By integrating the two processes of gradient calculation in the backpropagation algorithm and weight update in the write operation to the memristors, the proposed method accelerates the training process and also eliminates the external computing resources required in the existing method, such as multipliers and memories. Through numerical experiments, we demonstrated that the proposed method achieves twice faster convergence of the training process than the existing method, while retaining the same level of the accuracy for the classification results.