FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversion
Wu, Xiaofeng Bojkovic, Velibor Gu, Bin Suo, Kun Zou, Kai
Wu, Xiaofeng
Bojkovic, Velibor
Gu, Bin
Suo, Kun
Zou, Kai
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
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Abstract
Spiking Neural Networks (SNNs) offer a promising avenue for energy-efficient computing compared with Artificial Neural Networks (ANNs), closely mirroring biological neural processes. However, this potential comes with inherent challenges in directly training SNNs through spatio-temporal backpropagation—stemming from the temporal dynamics of spiking neurons and their discrete signal processing—which necessitates alternative ways of training, most notably through ANN-SNN conversion. In this work, we introduce a lightweight Forward Temporal Bias Correction (FTBC) technique, aimed at enhancing conversion accuracy without the computational overhead. We ground our method on provided theoretical findings that through proper temporal bias calibration the expected error of ANN-SNN conversion can be reduced to be zero after each time step. We further propose a heuristic algorithm for finding the temporal bias only in the forward pass, thus eliminating the computational burden of backpropagation and we evaluate our method on CIFAR-10/100 and ImageNet datasets, achieving a notable increase in accuracy on all datasets. Codes are released at a GitHub repository.
Citation
X. Wu, V. Bojkovic, B. Gu, K. Suo, and K. Zou, “FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversion,” pp. 155–173, 2025, doi: 10.1007/978-3-031-72890-7_10.
Source
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Conference
European Conference on Computer Vision (ECCV)
Keywords
ANN-SNN Conversion, Spiking Neural Networks, Temporal Bias Correction
Subjects
Source
European Conference on Computer Vision (ECCV)
Publisher
Springer Nature