mlsanityData to 7 Oct 2026

A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration

arXiv 2106.06984 ↗First posted 13 Jun 2021cs.LGcited by 111

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Abstract (arXiv)

Spiking Neural Network (SNN) has been recognized as one of the next generation of neural networks. Conventionally, SNN can be converted from a pre-trained ANN by only replacing the ReLU activation to spike activation while keeping the parameters intact. Perhaps surprisingly, in this work we show that a proper way to calibrate the parameters during the conversion of ANN to SNN can bring significant improvements. We introduce SNN Calibration, a cheap but extraordinarily effective method by leveraging the knowledge within a pre-trained Artificial Neural Network (ANN). Starting by analyzing the conversion error and its propagation through layers theoretically, we propose the calibration algorithm that can correct the error layer-by-layer. The calibration only takes a handful number of training data and several minutes to finish. Moreover, our calibration algorithm can produce SNN with state-of-the-art architecture on the large-scale ImageNet dataset, including MobileNet and RegNet. Extensive experiments demonstrate the effectiveness and efficiency of our algorithm. For example, our advanced pipeline can increase up to 69% top-1 accuracy when converting MobileNet on ImageNet compared to baselines. Codes are released at https://github.com/yhhhli/SNN_Calibration.

Key facts. Cited by 111 papers in the map (data through 7 Oct 2026). Builds on 21 of its 40 references that are in the map. First posted on arXiv 13 Jun 2021.

Builds on

21 of 40 references.

References · 21

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,451
  4. 2016SGDR: Stochastic Gradient Descent with Warm Restartscited by 4,993
  5. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
  6. 2013Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computationcited by 2,031
  7. 2017Improved Regularization of Convolutional Neural Networks with Cutoutcited by 1,623
  8. 2020Designing Network Design Spacescited by 954
  9. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  10. 2018Bag of Tricks for Image Classification with Convolutional Neural Networkscited by 430
  11. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
  12. 2018Deep Learning in Spiking Neural Networkscited by 258
9 more
  1. 2018SLAYER: Spike Layer Error Reassignment in Timecited by 257
  2. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 236
  3. 2019Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detectioncited by 174
  4. 2020Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagationcited by 142
  5. 2020RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Networkcited by 141
  6. 2021Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networkscited by 134
  7. 2020DIET-SNN: Direct Input Encoding With Leakage and Threshold Optimization in Deep Spiking Neural Networkscited by 70
  8. 2016Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardwarecited by 39
  9. 2016Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networkscited by 31

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 20; 2023: 38; 2024: 18; 2025: 22; 2026: 11.

111 papers.

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  1. 2022Temporal Efficient Training of Spiking Neural Network via Gradient Re-weightingcited by 192
  2. 2022Attention Spiking Neural Networkscited by 103
  3. 2022Training High-Performance Low-Latency Spiking Neural Networks by Differentiation on Spike Representationcited by 89
  4. 2021Advancing Spiking Neural Networks towards Deep Residual Learningcited by 76
  5. 2022Neural Architecture Search for Spiking Neural Networkscited by 52
  6. 2022Neuromorphic Data Augmentation for Training Spiking Neural Networkscited by 52
  7. 2022Online Training Through Time for Spiking Neural Networkscited by 48
  8. 2024QKFormer: Hierarchical Spiking Transformer using Q-K Attentioncited by 46
  9. 2022Optimized Potential Initialization for Low-latency Spiking Neural Networkscited by 39
  10. 2023Reducing ANN-SNN Conversion Error through Residual Membrane Potentialcited by 39
  11. 2023SEENN: Towards Temporal Spiking Early-Exit Neural Networkscited by 39
  12. 2023Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikescited by 38
38 more
  1. 2023Towards Memory- and Time-Efficient Backpropagation for Training Spiking Neural Networkscited by 37
  2. 2023Ternary Spike: Learning Ternary Spikes for Spiking Neural Networkscited by 36
  3. 2023Membrane Potential Batch Normalization for Spiking Neural Networkscited by 35
  4. 2023Joint A-SNN: Joint Training of Artificial and Spiking Neural Networks via Self-Distillation and Weight Factorizationcited by 34
  5. 2023Spiking PointNet: Spiking Neural Networks for Point Cloudscited by 34
  6. 2023Direct Learning-Based Deep Spiking Neural Networks: A Reviewcited by 31
  7. 2022Efficient and Accurate Conversion of Spiking Neural Network with Burst Spikescited by 30
  8. 2024SpikeLLM: Scaling up Spiking Neural Network to Large Language Models via Saliency-based Spikingcited by 29
  9. 2023RMP-Loss: Regularizing Membrane Potential Distribution for Spiking Neural Networkscited by 27
  10. 2023Training Full Spike Neural Networks via Auxiliary Accumulation Pathwaycited by 26
  11. 2022Exploring Lottery Ticket Hypothesis in Spiking Neural Networkscited by 25
  12. 2022Exploring Temporal Information Dynamics in Spiking Neural Networkscited by 23
  13. 2022Real Spike: Learning Real-valued Spikes for Spiking Neural Networkscited by 22
  14. 2023Spiking Denoising Diffusion Probabilistic Modelscited by 18
  15. 2022Brain-inspired Multilayer Perceptron with Spiking Neuronscited by 16
  16. 2022Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentationcited by 15
  17. 2023Unleashing the Potential of Spiking Neural Networks by Dynamic Confidencecited by 14
  18. 2024Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networkscited by 14
  19. 2023A Hybrid ANN-SNN Architecture for Low-Power and Low-Latency Visual Perceptioncited by 13
  20. 2025Spiking Transformer:Introducing Accurate Addition-Only Spiking Self-Attention for Transformercited by 13
  21. 2023Reliable Evaluation of Adversarial Transferabilitycited by 12
  22. 2025Quantized Spike-driven Transformercited by 12
  23. 2023LaSNN: Layer-wise ANN-to-SNN Distillation for Effective and Efficient Training in Deep Spiking Neural Networkscited by 11
  24. 2023Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradientcited by 11
  25. 2024Efficient 3D Recognition with Event-driven Spike Sparse Convolutioncited by 11
  26. 2024LM-HT SNN: Enhancing the Performance of SNN to ANN Counterpart through Learnable Multi-hierarchical Threshold Modelcited by 10
  27. 2025Towards High-performance Spiking Transformers from ANN to SNN Conversioncited by 10
  28. 2025Temporal Separation with Entropy Regularization for Knowledge Distillation in Spiking Neural Networkscited by 10
  29. 2021Can Deep Neural Networks be Converted to Ultra Low-Latency Spiking Neural Networks?cited by 9
  30. 2024Enhancing Adversarial Robustness in SNNs with Sparse Gradientscited by 9
  31. 2025Differential Coding for Training-Free ANN-to-SNN Conversioncited by 9
  32. 2025Towards Effective and Sparse Adversarial Attack on Spiking Neural Networks via Breaking Invisible Surrogate Gradientscited by 9
  33. 2022SpikeSim: An end-to-end Compute-in-Memory Hardware Evaluation Tool for Benchmarking Spiking Neural Networkscited by 8
  34. 2023Optimising Event-Driven Spiking Neural Network with Regularisation and Cutoffcited by 8
  35. 2023SPIDE: A Purely Spike-based Method for Training Feedback Spiking Neural Networkscited by 8
  36. 2023Enhancing the Performance of Transformer-based Spiking Neural Networks by SNN-optimized Downsampling with Precise Gradient Backpropagationcited by 8
  37. 2023Knowing When to Stop: Delay-Adaptive Spiking Neural Network Classifiers with Reliability Guaranteescited by 8
  38. 2024Advancing Training Efficiency of Deep Spiking Neural Networks through Rate-based Backpropagationcited by 8

50 most cited of 111 All 111 (JSON)