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IDSNN: Towards High-Performance and Low-Latency SNN Training via Initialization and Distillation.

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This study introduces IDSNN, a new method for training spiking neural networks (SNNs) using artificial neural networks (ANNs). IDSNN significantly improves SNN accuracy and training speed, making them more practical for real-world applications.

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image classificationinitializationknowledge distillationspiking neural networks (SNNs)

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Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Machine Learning

Background:

  • Spiking neural networks (SNNs) offer biomimetic and efficient computation using spikes.
  • Current SNN training methods lead to low accuracy and high inference latency.
  • Existing approaches often involve direct training or conversion from artificial neural networks (ANNs).

Purpose of the Study:

  • To address the limitations of low accuracy and high latency in SNNs.
  • To propose a novel training pipeline, IDSNN, for enhanced SNN performance.
  • To leverage ANNs as a source for parameter initialization and knowledge distillation.

Main Methods:

  • Developed IDSNN, a training pipeline utilizing parameter initialization and knowledge distillation.
  • Employed ANNs as both parameter sources and knowledge teachers.
  • Evaluated IDSNN on CIFAR10 and CIFAR100 datasets.

Main Results:

  • Achieved competitive top-1 accuracy: 94.22% on CIFAR10 and 75.41% on CIFAR100.
  • Demonstrated low inference latency.
  • Showcased a 14× faster convergence speed compared to direct SNN training.

Conclusions:

  • IDSNN effectively overcomes accuracy and latency issues in SNNs.
  • The proposed method maximizes knowledge transfer from ANNs.
  • IDSNN exhibits significant practical value for SNN applications due to improved efficiency and faster convergence.