Boosting Throughput and Efficiency of Hardware Spiking Neural Accelerators Using Time Compression Supporting Multiple Spike Codes

Frontiers in Neuroscience
Changqing XuPeng Li

Abstract

Spiking neural networks (SNNs) are the third generation of neural networks and can explore both rate and temporal coding for energy-efficient event-driven computation. However, the decision accuracy of existing SNN designs is contingent upon processing a large number of spikes over a long period. Nevertheless, the switching power of SNN hardware accelerators is proportional to the number of spikes processed while the length of spike trains limits throughput and static power efficiency. This paper presents the first study on developing temporal compression to significantly boost throughput and reduce energy dissipation of digital hardware SNN accelerators while being applicable to multiple spike codes. The proposed compression architectures consist of low-cost input spike compression units, novel input-and-output-weighted spiking neurons, and reconfigurable time constant scaling to support large and flexible time compression ratios. Our compression architectures can be transparently applied to any given pre-designed SNNs employing either rate or temporal codes while incurring minimal modification of the neural models, learning algorithms, and hardware design. Using spiking speech and image recognition datasets, we demonstrate th...Continue Reading

References

Oct 23, 2001·Neural Networks : the Official Journal of the International Neural Network Society·S ThorpeR Van Rullen
Feb 3, 2015·IEEE Transactions on Neural Networks and Learning Systems·Yong ZhangYoonsuck Choe
Feb 27, 2018·Frontiers in Neuroscience·Jithendar AnumulaShih-Chii Liu
Jan 24, 2019·Frontiers in Neuroscience·Davide ZambranoSander M Bohté

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