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Self-architectural knowledge distillation for spiking neural networks.

Haonan Qiu¹, Munan Ning¹, Zeyin Song¹

¹Peking University, School of Electronic and Computer Engineering, Shenzhen Graduate School, China.

Neural Networks : the Official Journal of the International Neural Network Society

|June 28, 2024

Summary

Self-Architectural Knowledge Distillation (SAKD) enhances spiking neural networks (SNNs) by balancing performance and low latency. This method achieves state-of-the-art results on benchmarks with fewer time steps, improving SNN efficiency.

Keywords:

ANN-to-SNN Image classification Knowledge distillation Semantic segmentation Spiking neural networks

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Area of Science:

Artificial Intelligence
Computational Neuroscience
Machine Learning

Background:

Spiking neural networks (SNNs) offer biological plausibility and low-energy computation potential for neuromorphic hardware.
Current SNN training methods face a trade-off between performance (ANN-to-SNN conversion) and latency (direct training).

Purpose of the Study:

To address the performance-latency trade-off in spiking neural networks.
To introduce a novel method, Self-Architectural Knowledge Distillation (SAKD), for training SNNs.

Main Methods:

Proposed Self-Architectural Knowledge Distillation (SAKD) using a bilevel teacher-student strategy.
Level-1: Direct transfer of pre-trained Artificial Neural Network (ANN) weights to SNNs.
Level-2: Encouraging SNNs to mimic ANN behavior, including intermediate features and final outputs.

Main Results:

Achieved new state-of-the-art (SOTA) performance on classification benchmarks with minimal time steps.
Spiking-ResNet34 attained 70.04% Top-1 accuracy on ImageNet-1K with 4 time steps.
SEW-ResNet152 achieved 77.30% Top-1 accuracy on ImageNet-1K, setting a new SOTA for SNNs.
Demonstrated strong generalization on downstream tasks like object detection and semantic segmentation.

Conclusions:

SAKD effectively balances high performance and low latency in SNNs.
The proposed framework shows significant advancements for SNNs in energy-efficient AI.
SAKD offers a promising direction for future SNN research and applications.