Robustness Evaluation of Restricted Boltzmann Machine against Memory and Logic Error

Yasushi FUKUDA  Zule XU  Takayuki KAWAHARA  

IEICE TRANSACTIONS on Electronics   Vol.E100-C   No.12   pp.1118-1121
Publication Date: 2017/12/01
Online ISSN: 1745-1353
DOI: 10.1587/transele.E100.C.1118
Type of Manuscript: BRIEF PAPER
Category: Integrated Electronics
IoT,  neural network,  RBM,  DBN,  soft error,  

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In an IoT system, neural networks have the potential to perform advanced information processing in various environments. To clarify this, the robustness of a restricted Boltzmann machine (RBM) used for deep neural networks, such as a deep belief network (DBN), was studied in this paper. Even if memory or logic errors occurred in the circuit operating in the RBM while pre-training the DBN, they did not affect the identification rate of the DBN, showing the robustness of the RBM. In addition, robustness against soft errors was evaluated. The soft errors had almost no influence on the RBM unless they were as large as 1012 times or more in the 50-nm CMOS process.