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Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks

arXiv 2311.14265 ↗First posted 24 Nov 2023cs.CVcited by 0

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Abstract (arXiv)

Spiking Neural Networks (SNNs) have emerged as a promising energy-efficient alternative to traditional Artificial Neural Networks (ANNs). Despite this, bridging the performance gap with ANNs in practical scenarios remains a significant challenge. This paper focuses on addressing the dual objectives of enhancing the performance and efficiency of SNNs through the established SNN Calibration conversion framework. Inspired by the biological nervous system, we propose a novel Adaptive-Firing Neuron Model (AdaFire) that dynamically adjusts firing patterns across different layers, substantially reducing conversion errors within limited timesteps. Moreover, to meet our efficiency objectives, we propose two novel strategies: an Sensitivity Spike Compression (SSC) technique and an Input-aware Adaptive Timesteps (IAT) technique. These techniques synergistically reduce both energy consumption and latency during the conversion process, thereby enhancing the overall efficiency of SNNs. Extensive experiments demonstrate our approach outperforms state-of-the-art SNNs methods, showcasing superior performance and efficiency in 2D, 3D, and event-driven classification, as well as object detection and segmentation tasks.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 57 references that are in the map. First posted on arXiv 24 Nov 2023.

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References · 31

  1. 2017Attention Is All You Needcited by 60,361
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  5. 2017On Calibration of Modern Neural Networkscited by 4,960
  6. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 626
  7. 2017BranchyNet: Fast Inference via Early Exiting from Deep Neural Networkscited by 596
  8. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 345
  9. 2022Spikformer: When Spiking Neural Network Meets Transformercited by 271
  10. 2020ZeroQ: A Novel Zero Shot Quantization Frameworkcited by 269
  11. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 198
  12. 2019Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detectioncited by 177
19 more
  1. 2021Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networkscited by 139
  2. 2023Optimal ANN-SNN Conversion for High-accuracy and Ultra-low-latency Spiking Neural Networkscited by 122
  3. 2021A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibrationcited by 117
  4. 2021Temporal-wise Attention Spiking Neural Networks for Event Streams Classificationcited by 106
  5. 2022Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representationcited by 89
  6. 2021Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networkscited by 78
  7. 2023Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Modelcited by 69
  8. 2022Neuromorphic Data Augmentation for Training Spiking Neural Networkscited by 52
  9. 2023SEENN: Towards Temporal Spiking Early-Exit Neural Networkscited by 41
  10. 2023Reducing ANN-SNN Conversion Error through Residual Membrane Potentialcited by 40
  11. 2023Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikescited by 40
  12. 2022Masked Spiking Transformercited by 39
  13. 2018Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networkscited by 35
  14. 2021Event-driven Vision and Control for UAVs on a Neuromorphic Chipcited by 34
  15. 2022Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikescited by 30
  16. 2021CarSNN: An Efficient Spiking Neural Network for Event-Based Autonomous Cars on the Loihi Neuromorphic Research Processorcited by 27
  17. 2020TCL: an ANN-to-SNN Conversion with Trainable Clipping Layerscited by 20
  18. 2023Spiking Denoising Diffusion Probabilistic Modelscited by 19
  19. 2018Adaptive motor control and learning in a spiking neural network realised on a mixed-signal neuromorphic processorcited by 5

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