A Power-Efficient Binary-Weight Spiking Neural Network Architecture for Real-Time Object Classification

• URL: http://arxiv.org/abs/2003.06310v1
• Date: Thu, 12 Mar 2020 11:25:00 GMT
• Title: A Power-Efficient Binary-Weight Spiking Neural Network Architecture for Real-Time Object Classification
• Authors: Pai-Yu Tan, Po-Yao Chuang, Yen-Ting Lin, Cheng-Wen Wu, and Juin-Ming Lu
• Abstract summary: We propose a binary-weight spiking neural network (BW-SNN) hardware architecture for low-power real-time object classification on edge platforms. This design stores a full neural network on-chip, and hence requires no off-chip bandwidth.
• Score: 1.5291703721641183
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural network hardware is considered an essential part of future edge devices. In this paper, we propose a binary-weight spiking neural network (BW-SNN) hardware architecture for low-power real-time object classification on edge platforms. This design stores a full neural network on-chip, and hence requires no off-chip bandwidth. The proposed systolic array maximizes data reuse for a typical convolutional layer. A 5-layer convolutional BW-SNN hardware is implemented in 90nm CMOS. Compared with state-of-the-art designs, the area cost and energy per classification are reduced by 7$\times$ and 23$\times$, respectively, while also achieving a higher accuracy on the MNIST benchmark. This is also a pioneering SNN hardware architecture that supports advanced CNN architectures.

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