# Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1612.04052 · HTML: https://mlsanity.com/paper/1612.04052/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2016.

## Built on by (most cited first, 30 of 31)

- [Temporal Efficient Training of Spiking Neural Network via Gradient Re-weighting](https://mlsanity.com/paper/2202.11946/) (2022)
- [Optimal Conversion of Conventional Artificial Neural Networks to Spiking Neural Networks](https://mlsanity.com/paper/2103.00476/) (2021)
- [A Free Lunch From ANN: Towards Efficient, Accurate Spiking Neural Networks Calibration](https://mlsanity.com/paper/2106.06984/) (2021)
- [Optimal ANN-SNN Conversion for Fast and Accurate Inference in Deep Spiking Neural Networks](https://mlsanity.com/paper/2105.11654/) (2021)
- [Neuromorphic Data Augmentation for Training Spiking Neural Networks](https://mlsanity.com/paper/2203.06145/) (2022)
- [Optimized Potential Initialization for Low-latency Spiking Neural Networks](https://mlsanity.com/paper/2202.01440/) (2022)
- [SEENN: Towards Temporal Spiking Early-Exit Neural Networks](https://mlsanity.com/paper/2304.01230/) (2023)
- [Reducing ANN-SNN Conversion Error through Residual Membrane Potential](https://mlsanity.com/paper/2302.02091/) (2023)
- [A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection](https://mlsanity.com/paper/2104.10719/) (2021)
- [Exploiting Noise as a Resource for Computation and Learning in Spiking Neural Networks](https://mlsanity.com/paper/2305.16044/) (2023)
- [Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradient](https://mlsanity.com/paper/2304.13098/) (2023)
- [Converting Artificial Neural Networks to Spiking Neural Networks via Parameter Calibration](https://mlsanity.com/paper/2205.10121/) (2022)
- [Adversarially Robust Spiking Neural Networks Through Conversion](https://mlsanity.com/paper/2311.09266/) (2023)
- [Low Latency Conversion of Artificial Neural Network Models to Rate-encoded Spiking Neural Networks](https://mlsanity.com/paper/2211.08410/) (2022)
- [A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks](https://mlsanity.com/paper/2305.19306/) (2023)
- [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)
- [Sign Gradient Descent-based Neuronal Dynamics: ANN-to-SNN Conversion Beyond ReLU Network](https://mlsanity.com/paper/2407.01645/) (2024)
- [Addressing the speed-accuracy simulation trade-off for adaptive spiking neurons](https://mlsanity.com/paper/2311.11390/) (2023)
- [Efficient ANN-SNN Conversion with Error Compensation Learning](https://mlsanity.com/paper/2506.01968/) (2025)
- [Ultra-low-power Image Classification on Neuromorphic Hardware](https://mlsanity.com/paper/2309.16795/) (2023)
- [Membership Privacy Evaluation in Deep Spiking Neural Networks](https://mlsanity.com/paper/2409.19413/) (2024)
- [FAS: Fast ANN-SNN Conversion for Spiking Large Language Models](https://mlsanity.com/paper/2502.04405/) (2025)
- [Temporal Flexibility in Spiking Neural Networks: Towards Generalization Across Time Steps and Deployment Friendliness](https://mlsanity.com/paper/2503.17394/) (2025)
- [Joint ANN-SNN Co-training for Object Localization and Image Segmentation](https://mlsanity.com/paper/2303.12738/) (2023)
- [PASCAL: Precise and Efficient ANN- SNN Conversion using Spike Accumulation and Adaptive Layerwise Activation](https://mlsanity.com/paper/2505.01730/) (2025)
- [Spiking Heterogeneous Graph Attention Networks](https://mlsanity.com/paper/2601.02401/) (2025)
- [Spiking Neural Network: a low power solution for physical layer authentication](https://mlsanity.com/paper/2505.12647/) (2025)
- [Integer Binary-Range Alignment Neuron for Spiking Neural Networks](https://mlsanity.com/paper/2506.05679/) (2025)
- [Large Language Models Inference Engines based on Spiking Neural Networks](https://mlsanity.com/paper/2510.00133/) (2025)

## Abstract (arXiv)

Deep convolutional neural networks (CNNs) have shown great potential for numerous real-world machine learning applications, but performing inference in large CNNs in real-time remains a challenge. We have previously demonstrated that traditional CNNs can be converted into deep spiking neural networks (SNNs), which exhibit similar accuracy while reducing both latency and computational load as a consequence of their data-driven, event-based style of computing. Here we provide a novel theory that explains why this conversion is successful, and derive from it several new tools to convert a larger and more powerful class of deep networks into SNNs. We identify the main sources of approximation errors in previous conversion methods, and propose simple mechanisms to fix these issues. Furthermore, we develop spiking implementations of common CNN operations such as max-pooling, softmax, and batch-normalization, which allow almost loss-less conversion of arbitrary CNN architectures into the spiking domain. Empirical evaluation of different network architectures on the MNIST and CIFAR10 benchmarks leads to the best SNN results reported to date.