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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| Vydáno v: | Proceedings - Design, Automation, and Test in Europe Conference and Exhibition s. 1 - 7 |
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| Jazyk: | angličtina |
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31.03.2025
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| ISSN: | 1558-1101 |
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| Abstract | 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 simulation or synchronized hardware circuitry, is incompatible with the event-driven nature, thus resulting in poor performance and energy efficiency. The challenge arises from the fact that neuron computations across multiple time steps result in increased latency and energy consumption. To overcome this bottleneck and leverage the full potential of SNNs, we propose TAIL, a pioneering temporal asynchronous execution mechanism for SNNs driven by a comprehensive analysis of SNN computations. Additionally, we propose an efficient dataflow design to support SNN inference, enabling concurrent computation of various time steps across multiple layers for optimal Processing Element (PE) utilization. Our evaluations show that TAIL greatly improves the performance of SNN inference, achieving a 6.94× speedup and a 6.97× increase in energy efficiency on current SNN computing platforms. |
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| AbstractList | 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 simulation or synchronized hardware circuitry, is incompatible with the event-driven nature, thus resulting in poor performance and energy efficiency. The challenge arises from the fact that neuron computations across multiple time steps result in increased latency and energy consumption. To overcome this bottleneck and leverage the full potential of SNNs, we propose TAIL, a pioneering temporal asynchronous execution mechanism for SNNs driven by a comprehensive analysis of SNN computations. Additionally, we propose an efficient dataflow design to support SNN inference, enabling concurrent computation of various time steps across multiple layers for optimal Processing Element (PE) utilization. Our evaluations show that TAIL greatly improves the performance of SNN inference, achieving a 6.94× speedup and a 6.97× increase in energy efficiency on current SNN computing platforms. |
| Author | Liu, Fangxin Huang, Shiyuan Sun, Qi Song, Zhuoran Jiang, Li Lyu, Dongxu Wang, Zongwu Li, Haomin Yang, Ning |
| Author_xml | – sequence: 1 givenname: Haomin surname: Li fullname: Li, Haomin email: haominli@sjtu.edu.cn organization: Shanghai Jiao Tong University – sequence: 2 givenname: Fangxin surname: Liu fullname: Liu, Fangxin email: liufangxin@sjtu.edu.cn organization: Shanghai Jiao Tong University – sequence: 3 givenname: Zongwu surname: Wang fullname: Wang, Zongwu organization: Shanghai Jiao Tong University – sequence: 4 givenname: Dongxu surname: Lyu fullname: Lyu, Dongxu organization: Shanghai Jiao Tong University – sequence: 5 givenname: Shiyuan surname: Huang fullname: Huang, Shiyuan organization: Shanghai Jiao Tong University – sequence: 6 givenname: Ning surname: Yang fullname: Yang, Ning organization: Shanghai Jiao Tong University – sequence: 7 givenname: Qi surname: Sun fullname: Sun, Qi organization: Zhejiang University – sequence: 8 givenname: Zhuoran surname: Song fullname: Song, Zhuoran organization: Shanghai Jiao Tong University – sequence: 9 givenname: Li surname: Jiang fullname: Jiang, Li email: ljiang_cs@sjtu.edu.cn organization: Shanghai Jiao Tong University |
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| SubjectTerms | Asynchronous Execution Brain-inspired Computing Computational modeling Energy consumption Energy efficiency Hardware Parallel processing Performance gain Software Spiking neural networks Synchronization Tail |
| Title | TAIL: Exploiting Temporal Asynchronous Execution for Efficient Spiking Neural Networks with Inter-Layer Parallelism |
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