Training-free Conversion of Pretrained ANNs to SNNs for Low-Power and High-Performance Applications
<|reference_start|>Training-free Conversion of Pretrained ANNs to SNNs for Low-Power and High-Performance Applications: Spiking Neural Networks (SNNs) have emerged as a promising substitute for Artificial Neural Networks (ANNs) due to their advantages of fast inference and low power consumption. However, the lack of efficient training algorithms has hindered their widespread adoption. Existing supervised learning algorithms for SNNs require significantly more memory and time than their ANN counterparts. Even commonly used ANN-SNN conversion methods necessitate re-training of ANNs to enhance conversion efficiency, incurring additional computational costs. To address these challenges, we propose a novel training-free ANN-SNN conversion pipeline. Our approach directly converts pre-trained ANN models into high-performance SNNs without additional training. The conversion pipeline includes a local-learning-based threshold balancing algorithm, which enables efficient calculation of the optimal thresholds and fine-grained adjustment of threshold value by channel-wise scaling. We demonstrate the scalability of our framework across three typical computer vision tasks: image classification, semantic segmentation, and object detection. This showcases its applicability to both classification and regression tasks. Moreover, we have evaluated the energy consumption of the converted SNNs, demonstrating their superior low-power advantage compared to conventional ANNs. Our training-free algorithm outperforms existing methods, highlighting its practical applicability and efficiency. This approach simplifies the deployment of SNNs by leveraging open-source pre-trained ANN models and neuromorphic hardware, enabling fast, low-power inference with negligible performance reduction.<|reference_end|>
@article{bu2024training-free,
    title={Training-free Conversion of Pretrained ANNs to SNNs for Low-Power and
  High-Performance Applications},
    author={Tong Bu, Maohua Li and Zhaofei Yu},
    journal={arXiv preprint arXiv:2409.03368},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03368},
    primaryClass={cs.NE}
}