On the fault tolerance of a clustered single-electron neural network for differential enhancement

IEICE Electronics Express

Tetsuya Asai²  Takahide Oya²  Yusuf Leblebici¹  Yoshihito Amemiya²  Alexandre Schmid¹ 

[1] Microelectronic Systems Laboratory, Swiss Federal Institute of Technology (EPFL);Graduate School of Information Science and Technology, Hokkaido University

关键词: single-electron circuit;    neural network;    fault tolerance;   

DOI  :  10.1587/elex.2.76

学科分类：电子、光学、磁材料

来源: Denshi Jouhou Tsuushin Gakkai

【 摘 要 】

References(5)Cited-By(5)A clustered neural network, in which neuronal information is represented by a cluster (population of neurons), rather than a single neuron, is a possible solution to construct fault-tolerant single-electron circuits. We designed single-electron circuits based on a clustered neural network that performs differential enhancement where differences between the cluster's outputs receiving various magnitudes of inputs are enhanced after the processing. Simulation results showed that the degradation of the performance of the clustered single-electron neural network was significantly lower than that of a non-clustered network, which indicates that this approach is one possible way to construct fault-tolerant computing systems on nanodevices.

【 授权许可】

Unknown