Exploiting Temporal-Unrolled Parallelism for Energy-Efficient SNN Acceleration-Reference-Cited by-同舟云学术

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Exploiting Temporal-Unrolled Parallelism for Energy-Efficient SNN Acceleration

Published:2024-10 Issue:10 Volume:35 Page:1749-1764
ISSN:1045-9219
Container-title:IEEE Transactions on Parallel and Distributed Systems
language:
Short-container-title:IEEE Trans. Parallel Distrib. Syst.

Author:

Liu Fangxin¹^ORCID,Wang Zongwu¹,Zhao Wenbo²,Yang Ning¹,Chen Yongbiao¹^ORCID,Huang Shiyuan¹^ORCID,Li Haomin¹^ORCID,Yang Tao³,Pei Songwen⁴^ORCID,Liang Xiaoyao¹,Jiang Li¹^ORCID

Affiliation:

1. Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

2. PDD Holdings Inc., Atlanta, GA, USA

3. Huawei Technologies Company Ltd., Shenzhen, China

4. Computer Science and Engineering Department, University of Shanghai for Science and Technology, Shanghai, China

Funder

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/71/10631781/10561563.pdf?arnumber=10561563

Reference45 articles.

1. Towards spike-based machine intelligence with neuromorphic computing

2. Rethinking the performance comparison between SNNS and ANNS

3. Enabling deep spiking neural networks with hybrid conversion and spike timing dependent backpropagation;Rathi

4. Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-linear Activations

5. SpinalFlow: An Architecture and Dataflow Tailored for Spiking Neural Networks

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