Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks

• URL: http://arxiv.org/abs/2304.10749v5
• Date: Tue, 7 Nov 2023 07:54:54 GMT
• Title: Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks
• Authors: Wenxuan Pan, Feifei Zhao, Guobin Shen, Yi Zeng
• Abstract summary: We propose a Multi-Scale Evolutionary Neural Architecture Search (MSE-NAS) for Spiking Neural Networks (SNNs) MSE-NAS evolves individual neuron operation, self-organized integration of multiple circuit motifs, and global connectivity across motifs through a brain-inspired indirect evaluation function, Representational Dissimilarity Matrices (RDMs) The proposed algorithm achieves state-of-the-art (SOTA) performance with shorter simulation steps on static datasets and neuromorphic datasets.
• Score: 7.271032282434803
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Spiking Neural Networks (SNNs) have received considerable attention not only for their superiority in energy efficiency with discrete signal processing but also for their natural suitability to integrate multi-scale biological plasticity. However, most SNNs directly adopt the structure of the well-established Deep Neural Networks (DNNs), and rarely automatically design Neural Architecture Search (NAS) for SNNs. The neural motifs topology, modular regional structure and global cross-brain region connection of the human brain are the product of natural evolution and can serve as a perfect reference for designing brain-inspired SNN architecture. In this paper, we propose a Multi-Scale Evolutionary Neural Architecture Search (MSE-NAS) for SNN, simultaneously considering micro-, meso- and macro-scale brain topologies as the evolutionary search space. MSE-NAS evolves individual neuron operation, self-organized integration of multiple circuit motifs, and global connectivity across motifs through a brain-inspired indirect evaluation function, Representational Dissimilarity Matrices (RDMs). This training-free fitness function could greatly reduce computational consumption and NAS's time, and its task-independent property enables the searched SNNs to exhibit excellent transferability on multiple datasets. Furthermore, MSE-NAS show robustness against the training method and noise. Extensive experiments demonstrate that the proposed algorithm achieves state-of-the-art (SOTA) performance with shorter simulation steps on static datasets (CIFAR10, CIFAR100) and neuromorphic datasets (CIFAR10-DVS and DVS128-Gesture). The thorough analysis also illustrates the significant performance improvement and consistent bio-interpretability deriving from the topological evolution at different scales and the RDMs fitness function.

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