Author(s): Zhang, HL (Zhang, Huilin); Gang, C (Gang, Chen); Xu, C (Xu, Chen); Gong, GL (Gong, Guoliang); Lu, HX (Lu, Huaxiang)

Source: IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE DOI: 10.1109/TETCI.2021.3089328 Early Access Date: JUL 2021

Abstract: Based on recent research in artificial neural networks, researchers have focused on topics from brain-like computing based on the Von Neumann architecture to brain-inspired computing based on the integration of storage and calculation due to the large energy dissipation. Inspired by biological neural networks, people use superconducting devices, which have nonlinear dynamic characteristics, to construct synapse and neuron circuits. However, it is challenging to build large-scale superconducting spiking neural networks due to fan-ins and fan-outs of superconducting devices. In this paper, we present a criterion to scale up the superconducting neural network. Then, we present a three-layer fully connected superconducting spiking neural network construction scheme. Inspired by the biological neural network, we present a new training method based on frequency coding. After only 100 training iterations with one grayscale image of a digit, the learned three-layer fully connected superconducting spiking neural network has a recognition accuracy rate of 86.1% on the digit class. Moreover, the power dissipation of one spiking event in a superconducting synapse is $23.2{\bm{\ zJ}}$, which shows that the network has outstandingly high efficiency over existing biologically inspired neural networks.

Accession Number: WOS:000732278200001

ISSN: 2471-285X

Full Text: https://ieeexplore.ieee.org/document/9472872