mlsanityData to 7 Oct 2026

TCL: an ANN-to-SNN Conversion with Trainable Clipping Layers

arXiv 2008.04509 ↗First posted 11 Aug 2020cs.CVcited by 20

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Abstract (arXiv)

Spiking-neural-networks (SNNs) are promising at edge devices since the event-driven operations of SNNs provides significantly lower power compared to analog-neural-networks (ANNs). Although it is difficult to efficiently train SNNs, many techniques to convert trained ANNs to SNNs have been developed. However, after the conversion, a trade-off relation between accuracy and latency exists in SNNs, causing considerable latency in large size datasets such as ImageNet. We present a technique, named as TCL, to alleviate the trade-off problem, enabling the accuracy of 73.87% (VGG-16) and 70.37% (ResNet-34) for ImageNet with the moderate latency of 250 cycles in SNNs.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 11 Aug 2020.

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Citing papers per year: 2021: 1; 2022: 1; 2023: 5; 2024: 6; 2025: 7.

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  1. 2022Optimized Potential Initialization for Low-latency Spiking Neural Networkscited by 39
  2. 2023Reducing ANN-SNN Conversion Error through Residual Membrane Potentialcited by 39
  3. 2023Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikescited by 38
  4. 2024Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networkscited by 14
  5. 2025Spiking Transformer:Introducing Accurate Addition-Only Spiking Self-Attention for Transformercited by 13
  6. 2023Biologically inspired structure learning with reverse knowledge distillation for spiking neural networkscited by 11
  7. 2025Towards High-performance Spiking Transformers from ANN to SNN Conversioncited by 10
  8. 2021Can Deep Neural Networks be Converted to Ultra Low-Latency Spiking Neural Networks?cited by 9
  9. 2024Enhancing Adversarial Robustness in SNNs with Sparse Gradientscited by 9
  10. 2024Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networkcited by 6
  11. 2023Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentationcited by 5
  12. 2024FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversioncited by 5
8 more
  1. 2024Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamicscited by 2
  2. 2025Efficient ANN-SNN Conversion with Error Compensation Learningcited by 2
  3. 2025New Spiking Architecture for Multi-Modal Decision-Making in Autonomous Vehiclescited by 2
  4. 2023Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networkscited by 0
  5. 2024Deep activity propagation via weight initialization in spiking neural networkscited by 0
  6. 2025Temporal Misalignment in ANN-SNN Conversion and Its Mitigation via Probabilistic Spiking Neuronscited by 0
  7. 2025Large Language Models Inference Engines based on Spiking Neural Networkscited by 0
  8. 2025PrivSpike: Employing Homomorphic Encryption for Private Inference of Deep Spiking Neural Networkscited by 0