SNNOpt:脉冲神经网络的特定应用设计框架

2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS) Pub Date : 2023-06-11 DOI:10.1109/AICAS57966.2023.10168605

Jingyu He, Ziyang Shen, Fengshi Tian, Jinbo Chen, Jie Yang, M. Sawan, Hsiang-Ting Chen, P. Bogdan, C. Tsui

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引用次数: 0

摘要

我们提出了一种系统的应用专用硬件设计方法，用于设计峰值神经网络(SNNOpt)， SNNOpt由三个新阶段组成:1)基于ololiver - ricci -曲率(ORC)的架构感知网络划分，2)强化学习映射策略，以及3)用于NoC设计空间探索的贝叶斯优化算法。实验结果表明，SNNOpt比最先进的方法节省了47.45%的运行时间和58.64%的能源。

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SNNOpt: An Application-Specific Design Framework for Spiking Neural Networks

We propose a systematic application-specific hardware design methodology for designing Spiking Neural Network (SNN), SNNOpt, which consists of three novel phases: 1) an Olliver-Ricci-Curvature (ORC)-based architecture-aware network partitioning, 2) a reinforcement learning mapping strategy, and 3) a Bayesian optimization algorithm for NoC design space exploration. Experimental results show that SNNOpt achieves a 47.45% less runtime and 58.64% energy savings over state-of-the-art approaches.

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2023 IEEE 5th International Conference on Artificial Intelligence Circuits and Systems (AICAS)

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