# QP-SNN: Quantized and Pruned Spiking Neural Networks

arXiv: https://arxiv.org/abs/2502.05905 · HTML: https://mlsanity.com/paper/2502.05905/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2025.

## Built on by (most cited first, 14 of 14)

- [TP-Spikformer: Token Pruned Spiking Transformer](https://mlsanity.com/paper/2603.00527/) (2026)
- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [SpikeFit: Towards Optimal Deployment of Spiking Networks on Neuromorphic Hardware](https://mlsanity.com/paper/2510.15542/) (2025)
- [Energy-Efficient and Dequantization-Free Q-LLMs: A Spiking Neural Network Approach to Salient Value Mitigation](https://mlsanity.com/paper/2510.19498/) (2025)
- [BSO: Binary Spiking Online Optimization Algorithm](https://mlsanity.com/paper/2511.12502/) (2025)
- [Efficient Training of Spiking Neural Networks by Spike-aware Data Pruning](https://mlsanity.com/paper/2510.04098/) (2025)
- [SPINONet: Scalable Spiking Physics-informed Neural Operator for Computational Mechanics Applications](https://mlsanity.com/paper/2603.21674/) (2026)
- [Spiking Brain Compression: Post-Training Second-order Compression for Spiking Neural Networks](https://mlsanity.com/paper/2506.03996/) (2025)
- [SpikeSMOKE: Spiking Neural Networks for Monocular 3D Object Detection with Cross-Scale Gated Coding](https://mlsanity.com/paper/2506.07737/) (2025)
- [A Self-Ensemble Inspired Approach for Effective Training of Binary-Weight Spiking Neural Networks](https://mlsanity.com/paper/2508.12609/) (2025)
- [SAFA-SNN: Sparsity-Aware On-Device Few-Shot Class-Incremental Learning with Fast-Adaptive Structure of Spiking Neural Network](https://mlsanity.com/paper/2510.03648/) (2025)
- [CoNBONet: Conformalized Neuroscience-inspired Bayesian Operator Network for Reliability Analysis](https://mlsanity.com/paper/2603.21678/) (2026)
- [Spike-driven Large Language Model](https://mlsanity.com/paper/2604.16475/) (2026)
- [Efficient EEG Seizure Detection Using INT8 Quantization, Channel Pruning, and Spiking Neural Networks](https://mlsanity.com/paper/2607.16296/) (2026)

## Abstract (arXiv)

Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to encode information and operate in an asynchronous event-driven manner, offering a highly energy-efficient paradigm for machine intelligence. However, the current SNN community focuses primarily on performance improvement by developing large-scale models, which limits the applicability of SNNs in resource-limited edge devices. In this paper, we propose a hardware-friendly and lightweight SNN, aimed at effectively deploying high-performance SNN in resource-limited scenarios. Specifically, we first develop a baseline model that integrates uniform quantization and structured pruning, called QP-SNN baseline. While this baseline significantly reduces storage demands and computational costs, it suffers from performance decline. To address this, we conduct an in-depth analysis of the challenges in quantization and pruning that lead to performance degradation and propose solutions to enhance the baseline's performance. For weight quantization, we propose a weight rescaling strategy that utilizes bit width more effectively to enhance the model's representation capability. For structured pruning, we propose a novel pruning criterion using the singular value of spatiotemporal spike activities to enable more accurate removal of redundant kernels. Extensive experiments demonstrate that integrating two proposed methods into the baseline allows QP-SNN to achieve state-of-the-art performance and efficiency, underscoring its potential for enhancing SNN deployment in edge intelligence computing.