Bayesian Transferability Assessment for Spiking Neural Networks
Abstract: Brain-inspired spiking neural networks (SNNs) attract broad interest in neuromorphic computing but suffer the problem of being difficult to optimize. Concurrently, pre-trained models (PTMs) have become a foundation for developing and applying artificial intelligence. Therefore, it is expected that pre-trained SNNs can alleviate the optimization difficulty of training from scratch. However, with a lot of PTMs available in the model hubs, effectively selecting the most appropriate PTM for a given task remains a significant challenge, often necessitating exhaustive fine-tuning and grid-searching. While several solutions to this challenge have been proposed for the mainstream artificial neural network (ANNs), aimed at developing efficient methods to assess the transferability of PTMs on target tasks, the realm of SNNs remains unexplored. The currently most used transferability assessment method for ANNs predicts transferability in a Bayesian perspective. Feature maps extracted by the PTM backbone on the target task are used to calculate the maximum model evidence as the indicator of transferability. However, ANNs and SNNs differ in architecture, rendering the existing Bayesian method incompatible with SNNs. To solve this problem, this paper introduces a novel approach to using the feature maps averaged over the time domain to calculate maximum evidence. Our proposed $\textbf{M}$aximum $\textbf{E}$vidence method with $\textbf{A}$veraged $\textbf{F}$eatures (MEAF) demonstrates effectiveness for SNNs. Additionally, the current algorithm calculates maximum evidence in an iterative way. To accelerate the selection of PTMs, an approximation method is proposed to avoid iteration in the calculation of maximum evidence, significantly reducing time consumption. It is shown through experiment that the proposed MEAF method is effective for the transferability assessment of SNNs. MEAF outperforms information theory-based assessment methods such as LEEP and NCE, which can directly adapt to SNNs on neuromorphic datasets, underscoring its potential to streamline PTM selection and application in the realm of SNNs.
Submission Length: Regular submission (no more than 12 pages of main content)
Code: https://github.com/haohq19/meaf-snn
Assigned Action Editor: ~Mohammad_Emtiyaz_Khan1
Submission Number: 3776
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