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A Digital Neuromorphic Hardware for Spiking Neural Network

Authors :
Yisong Kuang
Xiaoxin Cui
Yuanning Fan
Chenglong Zou
Kefei Liu
Source :
2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC).
Publication Year :
2019
Publisher :
IEEE, 2019.

Abstract

The neuromorphic hardware with a non-von Neumann architecture has the advantage of highly-parallel and low-power. In this paper, a digital neuromorphic core with 1024 neurons, 1024 axons and a $1024\times 1024$ synaptic crossbar is designed, and the scalable network could be implemented based on the 2D mesh network on chip (NOC) architecture. The transformed deep spiking neural network (SNN) models can be mapped to our hardware directly, and show good application results. At the case of the full firing rate, the average power of a spike is 2.76E-08J, and for some image recognition tasks, the hardware power consumption is at the milliwatt level.

Details

Database :
OpenAIRE
Journal :
2019 IEEE International Conference on Electron Devices and Solid-State Circuits (EDSSC)
Accession number :
edsair.doi...........87498eef4bb902e75faa373ae9f4f2f5
Full Text :
https://doi.org/10.1109/edssc.2019.8754093