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

arXiv: https://arxiv.org/abs/2008.04509 · HTML: https://mlsanity.com/paper/2008.04509/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2020.

## Built on by (most cited first, 20 of 20)

- [Optimized Potential Initialization for Low-latency Spiking Neural Networks](https://mlsanity.com/paper/2202.01440/) (2022)
- [Reducing ANN-SNN Conversion Error through Residual Membrane Potential](https://mlsanity.com/paper/2302.02091/) (2023)
- [Bridging the Gap between ANNs and SNNs by Calibrating Offset Spikes](https://mlsanity.com/paper/2302.10685/) (2023)
- [Take A Shortcut Back: Mitigating the Gradient Vanishing for Training Spiking Neural Networks](https://mlsanity.com/paper/2401.04486/) (2024)
- [Spiking Transformer:Introducing Accurate Addition-Only Spiking Self-Attention for Transformer](https://mlsanity.com/paper/2503.00226/) (2025)
- [Biologically inspired structure learning with reverse knowledge distillation for spiking neural networks](https://mlsanity.com/paper/2304.09500/) (2023)
- [Towards High-performance Spiking Transformers from ANN to SNN Conversion](https://mlsanity.com/paper/2502.21193/) (2025)
- [Enhancing Adversarial Robustness in SNNs with Sparse Gradients](https://mlsanity.com/paper/2405.20355/) (2024)
- [Can Deep Neural Networks be Converted to Ultra Low-Latency Spiking Neural Networks?](https://mlsanity.com/paper/2112.12133/) (2021)
- [Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Network](https://mlsanity.com/paper/2412.16219/) (2024)
- [Hybrid Spiking Neural Network Fine-tuning for Hippocampus Segmentation](https://mlsanity.com/paper/2302.07328/) (2023)
- [FTBC: Forward Temporal Bias Correction for Optimizing ANN-SNN Conversion](https://mlsanity.com/paper/2403.18388/) (2024)
- [Efficient ANN-SNN Conversion with Error Compensation Learning](https://mlsanity.com/paper/2506.01968/) (2025)
- [Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics](https://mlsanity.com/paper/2408.01701/) (2024)
- [New Spiking Architecture for Multi-Modal Decision-Making in Autonomous Vehicles](https://mlsanity.com/paper/2512.01882/) (2025)
- [Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks](https://mlsanity.com/paper/2311.14265/) (2023)
- [Deep activity propagation via weight initialization in spiking neural networks](https://mlsanity.com/paper/2410.00580/) (2024)
- [Temporal Misalignment in ANN-SNN Conversion and Its Mitigation via Probabilistic Spiking Neurons](https://mlsanity.com/paper/2502.14487/) (2025)
- [Large Language Models Inference Engines based on Spiking Neural Networks](https://mlsanity.com/paper/2510.00133/) (2025)
- [PrivSpike: Employing Homomorphic Encryption for Private Inference of Deep Spiking Neural Networks](https://mlsanity.com/paper/2510.03995/) (2025)

## 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.