On-Chip Training Spiking Neural Networks Using Approximated Backpropagation With Analog Synaptic Devices.

ABSTRACT: Hardware-based spiking neural networks (SNNs) inspired by a biological nervous system are regarded as an innovative computing system with very low power consumption and massively parallel operation. To train SNNs with supervision, we propose an efficient on-chip training scheme approximating backpropagation algorithm suitable for hardware implementation. We show that the accuracy of the proposed scheme for SNNs is close to that of conventional artificial neural networks (ANNs) by using the stochastic characteristics of neurons. In a hardware configuration, gated Schottky diodes (GSDs) are used as synaptic devices, which have a saturated current with respect to the input voltage. We design the SNN system by using the proposed on-chip training scheme with the GSDs, which can update their con

SUBMITTER: Kwon D 

PROVIDER: S-EPMC7358558 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature