Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networks
September 10, 2018 ยท Declared Dead ยท ๐ Design Automation Conference
"No code URL or promise found in abstract"
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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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