TAIL: Exploiting Temporal Asynchronous Execution for Efficient Spiking Neural Networks with Inter-Layer Parallelism

Spiking neural networks (SNNs) are an alternative computational paradigm to artificial neural networks (ANNs) that have attracted attention due to their event-driven execution mechanisms, enabling extremely low energy consumption. However, the existing SNN execution model, based on software simulati...

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Veröffentlicht in:	Proceedings - Design, Automation, and Test in Europe Conference and Exhibition S. 1 - 7
Hauptverfasser:	Li, Haomin, Liu, Fangxin, Wang, Zongwu, Lyu, Dongxu, Huang, Shiyuan, Yang, Ning, Sun, Qi, Song, Zhuoran, Jiang, Li
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	EDAA 31.03.2025
Schlagworte:	Asynchronous Execution Brain-inspired Computing Computational modeling Energy consumption Energy efficiency Hardware Parallel processing Performance gain Software Spiking neural networks Synchronization Tail
ISSN:	1558-1101
Online-Zugang:	Volltext
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