Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks

September 10, 2018 ยท Declared Dead ยท ๐Ÿ› Design Automation Conference

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Authors Seongsik Park, Seijoon Kim, Hyeokjun Choe, Sungroh Yoon arXiv ID 1809.03142 Category cs.NE: Neural & Evolutionary Cross-listed cs.LG, q-bio.NC Citations 107 Venue Design Automation Conference Last Checked 4 months ago
Abstract
The spiking neural networks (SNNs) are considered as one of the most promising artificial neural networks due to their energy efficient computing capability. Recently, conversion of a trained deep neural network to an SNN has improved the accuracy of deep SNNs. However, most of the previous studies have not achieved satisfactory results in terms of inference speed and energy efficiency. In this paper, we propose a fast and energy-efficient information transmission method with burst spikes and hybrid neural coding scheme in deep SNNs. Our experimental results showed the proposed methods can improve inference energy efficiency and shorten the latency.
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