mlsanityData to 7 Oct 2026

Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks

arXiv 2308.04672 ↗First posted 9 Aug 2023cs.CV cs.AI cs.MMcited by 3

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Abstract (arXiv)

Brain-inspired Spiking Neural Networks (SNNs) have the characteristics of event-driven and high energy-efficient, which are different from traditional Artificial Neural Networks (ANNs) when deployed on edge devices such as neuromorphic chips. Most previous work focuses on SNNs training strategies to improve model performance and brings larger and deeper network architectures. It is difficult to deploy these complex networks on resource-limited edge devices directly. To meet such demand, people compress SNNs very cautiously to balance the performance and the computation efficiency. Existing compression methods either iteratively pruned SNNs using weights norm magnitude or formulated the problem as a sparse learning optimization. We propose an improved end-to-end Minimax optimization method for this sparse learning problem to better balance the model performance and the computation efficiency. We also demonstrate that jointly applying compression and finetuning on SNNs is better than sequentially, especially for extreme compression ratios. The compressed SNN models achieved state-of-the-art (SOTA) performance on various benchmark datasets and architectures. Our code is available at https://github.com/chenjallen/Resource-Constrained-Compression-on-SNN.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 55 of its 82 references that are in the map. First posted on arXiv 9 Aug 2023.

Builds on

55 of 82 references.

References · 55

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38 more
  1. 2016Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activationscited by 413
  2. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
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  38. 2021Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networkscited by 16

50 most cited of 55 All 55 (JSON)

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Cited by year

Citing papers per year: 2023: 1; 2024: 0; 2025: 2.

3 papers.

  1. 2025STAA-SNN: Spatial-Temporal Attention Aggregator for Spiking Neural Networkscited by 11
  2. 2023Brain-Inspired Efficient Pruning: Exploiting Criticality in Spiking Neural Networkscited by 2
  3. 2025NSPDI-SNN: An efficient lightweight SNN based on nonlinear synaptic pruning and dendritic integrationcited by 0