# Low Precision Quantization-aware Training in Spiking Neural Networks with Differentiable Quantization Function

arXiv: https://arxiv.org/abs/2305.19295 · HTML: https://mlsanity.com/paper/2305.19295/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 33 references that are in the map.
- First posted on arXiv 30 May 2023.

## Builds on (most cited first, 14 of 14)

- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networks](https://mlsanity.com/paper/2007.05785/) (2020)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [Direct Training for Spiking Neural Networks: Faster, Larger, Better](https://mlsanity.com/paper/1809.05793/) (2018)
- [Binary Neural Networks: A Survey](https://mlsanity.com/paper/2004.03333/) (2020)
- [Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and Better](https://mlsanity.com/paper/2106.08962/) (2021)
- [Quantization Networks](https://mlsanity.com/paper/1911.09464/) (2019)
- [STDP Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy Efficient Recognition](https://mlsanity.com/paper/1710.04734/) (2017)
- [Q-SpiNN: A Framework for Quantizing Spiking Neural Networks](https://mlsanity.com/paper/2107.01807/) (2021)
- [BS4NN: Binarized Spiking Neural Networks with Temporal Coding and Learning](https://mlsanity.com/paper/2007.04039/) (2020)
- [Spiking Neural Networks Hardware Implementations and Challenges: a Survey](https://mlsanity.com/paper/2005.01467/) (2020)
- [Neural Network Quantization for Efficient Inference: A Survey](https://mlsanity.com/paper/2112.06126/) (2021)
- [The fine line between dead neurons and sparsity in binarized spiking neural networks](https://mlsanity.com/paper/2201.11915/) (2022)
- [Hessian Aware Quantization of Spiking Neural Networks](https://mlsanity.com/paper/2104.14117/) (2021)

## Built on by (most cited first, 3 of 3)

- [SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence](https://mlsanity.com/paper/2310.16620/) (2023)
- [SpikeFit: Towards Optimal Deployment of Spiking Networks on Neuromorphic Hardware](https://mlsanity.com/paper/2510.15542/) (2025)
- [Spiking Brain Compression: Post-Training Second-order Compression for Spiking Neural Networks](https://mlsanity.com/paper/2506.03996/) (2025)

## Abstract (arXiv)

Deep neural networks have been proven to be highly effective tools in various domains, yet their computational and memory costs restrict them from being widely deployed on portable devices. The recent rapid increase of edge computing devices has led to an active search for techniques to address the above-mentioned limitations of machine learning frameworks. The quantization of artificial neural networks (ANNs), which converts the full-precision synaptic weights into low-bit versions, emerged as one of the solutions. At the same time, spiking neural networks (SNNs) have become an attractive alternative to conventional ANNs due to their temporal information processing capability, energy efficiency, and high biological plausibility. Despite being driven by the same motivation, the simultaneous utilization of both concepts has yet to be thoroughly studied. Therefore, this work aims to bridge the gap between recent progress in quantized neural networks and SNNs. It presents an extensive study on the performance of the quantization function, represented as a linear combination of sigmoid functions, exploited in low-bit weight quantization in SNNs. The presented quantization function demonstrates the state-of-the-art performance on four popular benchmarks, CIFAR10-DVS, DVS128 Gesture, N-Caltech101, and N-MNIST, for binary networks (64.05%, 95.45%, 68.71%, and 99.43% respectively) with small accuracy drops and up to 31× memory savings, which outperforms existing methods.