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Integer Binary-Range Alignment Neuron for Spiking Neural Networks

arXiv 2506.05679 ↗First posted 6 Jun 2025cs.NE cs.CVcited by 0

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Abstract (arXiv)

Spiking Neural Networks (SNNs) are noted for their brain-like computation and energy efficiency, but their performance lags behind Artificial Neural Networks (ANNs) in tasks like image classification and object detection due to the limited representational capacity. To address this, we propose a novel spiking neuron, Integer Binary-Range Alignment Leaky Integrate-and-Fire to exponentially expand the information expression capacity of spiking neurons with only a slight energy increase. This is achieved through Integer Binary Leaky Integrate-and-Fire and range alignment strategy. The Integer Binary Leaky Integrate-and-Fire allows integer value activation during training and maintains spike-driven dynamics with binary conversion expands virtual timesteps during inference. The range alignment strategy is designed to solve the spike activation limitation problem where neurons fail to activate high integer values. Experiments show our method outperforms previous SNNs, achieving 74.19% accuracy on ImageNet and 66.2% mAP@50 and 49.1% mAP@50:95 on COCO, surpassing previous bests with the same architecture by +3.45% and +1.6% and +1.8%, respectively. Notably, our SNNs match or exceed ANNs' performance with the same architecture, and the energy efficiency is improved by 6.3×.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 32 of its 49 references that are in the map. First posted on arXiv 6 Jun 2025.

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32 of 49 references.

References · 32

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  3. 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
  4. 2020End-to-End Object Detection with Transformerscited by 7,414
  5. 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 7,005
  6. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  7. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 345
  8. 2021Deep Residual Learning in Spiking Neural Networkscited by 331
  9. 2020Going Deeper With Directly-Trained Larger Spiking Neural Networkscited by 278
  10. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 245
  11. 2018Direct Training for Spiking Neural Networks: Faster, Larger, Bettercited by 233
  12. 2019Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detectioncited by 177
20 more
  1. 2020RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Networkcited by 145
  2. 2020Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagationcited by 144
  3. 2023Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networkscited by 122
  4. 2021A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibrationcited by 117
  5. 2022Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representationcited by 89
  6. 2023Deep Directly-Trained Spiking Neural Networks for Object Detectioncited by 77
  7. 2024Integer-Valued Training and Spike-Driven Inference Spiking Neural Network for High-performance and Energy-efficient Object Detectioncited by 67
  8. 2024Scaling Spike-driven Transformer with Efficient Spike Firing Approximation Trainingcited by 59
  9. 2023Reducing ANN-SNN Conversion Error through Residual Membrane Potentialcited by 40
  10. 2023Ternary Spike: Learning Ternary Spikes for Spiking Neural Networkscited by 38
  11. 2023Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorizationcited by 35
  12. 2023Membrane Potential Batch Normalization for Spiking Neural Networkscited by 35
  13. 2016Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networkscited by 31
  14. 2022Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikescited by 30
  15. 2023RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networkscited by 28
  16. 2023Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networkscited by 26
  17. 2022Real Spike: Learning Real-valued Spikes for Spiking Neural Networkscited by 22
  18. 2022Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentationcited by 15
  19. 2024BKDSNN: Enhancing the Performance of Learning-based Spiking Neural Networks Training with Blurred Knowledge Distillationcited by 11
  20. 2024Sign Gradient Descent-based Neuronal Dynamics: ANN-to-SNN Conversion Beyond ReLU Networkcited by 5

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