A novel memristor-based restricted Boltzmann machine for contrastive divergence

IEICE Electronics Express

Jishun Kuang¹  Yingjie Zhang¹  Zhiqiang You¹  Yan Chen²  Jing Zhang² 

[1] College of Computer Science and Electronic Engineering, Hunan University;College of Electrical and Information Engineering, Hunan University

关键词: contrastive divergence;    memristor;    restricted Boltzmann machine;   

DOI  :  10.1587/elex.15.20171062

学科分类：电子、光学、磁材料

来源: Denshi Jouhou Tsuushin Gakkai

【 摘 要 】

In this letter, we present a novel memristor-based restricted Boltzmann machine (RBM) system for training the brain-scale neural network applications. The proposed system delicately integrates the storage component of neuron outputs and the component of multiply-accumulate (MAC) in memory, allowed operating both of them in the same stage cycle and less memory access for the contrastive divergence (CD) training. Experimental results show that the proposed system delivers significantly 2770x speedup and less than 1% accuracy loss against the x86-CPU platform on RBM applications. On average, it achieves 2.3x faster performance and 2.1x better energy efficiency over recent state-of-the-art RBM training systems.

【 授权许可】

CC BY