mlsanityData to 7 Oct 2026

AutoSNN: Towards Energy-Efficient Spiking Neural Networks

arXiv 2201.12738 ↗First posted 30 Jan 2022cs.NE cs.AI cs.LGcited by 40

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Abstract (arXiv)

Spiking neural networks (SNNs) that mimic information transmission in the brain can energy-efficiently process spatio-temporal information through discrete and sparse spikes, thereby receiving considerable attention. To improve accuracy and energy efficiency of SNNs, most previous studies have focused solely on training methods, and the effect of architecture has rarely been studied. We investigate the design choices used in the previous studies in terms of the accuracy and number of spikes and figure out that they are not best-suited for SNNs. To further improve the accuracy and reduce the spikes generated by SNNs, we propose a spike-aware neural architecture search framework called AutoSNN. We define a search space consisting of architectures without undesirable design choices. To enable the spike-aware architecture search, we introduce a fitness that considers both the accuracy and number of spikes. AutoSNN successfully searches for SNN architectures that outperform hand-crafted SNNs in accuracy and energy efficiency. We thoroughly demonstrate the effectiveness of AutoSNN on various datasets including neuromorphic datasets.

Key facts. Cited by 40 papers in the map (data through 7 Oct 2026). Builds on 45 of its 69 references that are in the map. First posted on arXiv 30 Jan 2022.

Builds on

45 of 69 references.

References · 45

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
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33 more
  1. 2018Efficient Neural Architecture Search via Parameter Sharingcited by 831
  2. 2019Once-for-All: Train One Network and Specialize it for Efficient Deploymentcited by 778
  3. 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 666
  4. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 604
  5. 2013Network In Networkcited by 600
  6. 2018FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Searchcited by 480
  7. 2019Single Path One-Shot Neural Architecture Search with Uniform Samplingcited by 411
  8. 2017Large-Scale Evolution of Image Classifierscited by 348
  9. 2020Incorporating Learnable Membrane Time Constant to Enhance Learning of Spiking Neural Networkscited by 340
  10. 2021Deep Residual Learning in Spiking Neural Networkscited by 326
  11. 2020Going Deeper With Directly-Trained Larger Spiking Neural Networkscited by 268
  12. 2019Searching for A Robust Neural Architecture in Four GPU Hourscited by 258
  13. 2019Random Search and Reproducibility for Neural Architecture Searchcited by 253
  14. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 236
  15. 2018Direct Training for Spiking Neural Networks: Faster, Larger, Bettercited by 222
  16. 2019Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detectioncited by 174
  17. 2020RMP-SNN: Residual Membrane Potential Neuron for Enabling Deeper High-Accuracy and Low-Latency Spiking Neural Networkcited by 141
  18. 2019Enabling Spike-based Backpropagation for Training Deep Neural Network Architecturescited by 119
  19. 2021LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Searchcited by 82
  20. 2019DetNAS: Backbone Search for Object Detectioncited by 80
  21. 2020Revisiting Batch Normalization for Training Low-latency Deep Spiking Neural Networks from Scratchcited by 76
  22. 2020GreedyNAS: Towards Fast One-Shot NAS with Greedy Supernetcited by 69
  23. 2022Neural Architecture Search for Spiking Neural Networkscited by 52
  24. 2020Weight-Sharing Neural Architecture Search: A Battle to Shrink the Optimization Gapcited by 50
  25. 2020T2FSNN: Deep Spiking Neural Networks with Time-to-first-spike Codingcited by 45
  26. 2020Comparing SNNs and RNNs on Neuromorphic Vision Datasets: Similarities and Differencescited by 39
  27. 2021HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformerscited by 39
  28. 2020Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Searchcited by 38
  29. 2018Fast and Efficient Information Transmission with Burst Spikes in Deep Spiking Neural Networkscited by 34
  30. 2020DCNAS: Densely Connected Neural Architecture Search for Semantic Image Segmentationcited by 33
  31. 2021Neural Architecture Search with Random Labelscited by 33
  32. 2020Hit-Detector: Hierarchical Trinity Architecture Search for Object Detectioncited by 32
  33. 2020Low-activity supervised convolutional spiking neural networks applied to speech commands recognitioncited by 19

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 13; 2024: 15; 2025: 4; 2026: 5.

40 papers.

Sort:

  1. 2023SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligencecited by 163
  2. 2023Direct Learning-Based Deep Spiking Neural Networks: A Reviewcited by 31
  3. 2023Inherent Redundancy in Spiking Neural Networkscited by 13
  4. 2023A Unified Framework for Soft Threshold Pruningcited by 12
  5. 2024SpikeNAS: A Fast Memory-Aware Neural Architecture Search Framework for Spiking Neural Network-based Embedded AI Systemscited by 12
  6. 2023Uncovering the Representation of Spiking Neural Networks Trained with Surrogate Gradientcited by 11
  7. 2024SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Recognition with Jaccard Attentive Spiking Neural Networkcited by 9
  8. 2024Self-Distillation Learning Based on Temporal-Spatial Consistency for Spiking Neural Networkscited by 9
  9. 2023Efficient Deep Spiking Multi-Layer Perceptrons with Multiplication-Free Inferencecited by 7
  10. 2024Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stackcited by 7
  11. 2023AutoST: Training-free Neural Architecture Search for Spiking Transformerscited by 6
  12. 2024Toward Large-scale Spiking Neural Networks: A Comprehensive Survey and Future Directionscited by 6
28 more
  1. 2022Spiking GATs: Learning Graph Attentions via Spiking Neural Networkcited by 3
  2. 2023Exploiting High Performance Spiking Neural Networks with Efficient Spiking Patternscited by 3
  3. 2023Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networkscited by 3
  4. 2023Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networkscited by 3
  5. 2022Training Robust Spiking Neural Networks on Neuromorphic Data with Spatiotemporal Fragmentscited by 2
  6. 2024A survey on learning models of spiking neural membrane systems and spiking neural networkscited by 2
  7. 2024SpiKernel: A Kernel Size Exploration Methodology for Improving Accuracy of the Embedded Spiking Neural Network Systemscited by 2
  8. 2025Edge Intelligence with Spiking Neural Networkscited by 2
  9. 2023Training Robust Spiking Neural Networks with ViewPoint Transform and SpatioTemporal Stretchingcited by 1
  10. 2023Brain-inspired Evolutionary Architectures for Spiking Neural Networkscited by 1
  11. 2024Parallel Hyperparameter Optimization Of Spiking Neural Networkcited by 1
  12. 2024SparrowSNN: A Hardware/software Co-design for Energy Efficient ECG Classificationcited by 1
  13. 2024NeuroNAS: Enhancing Efficiency of Neuromorphic In-Memory Computing for Intelligent Mobile Agents through Hardware-Aware Spiking Neural Architecture Searchcited by 1
  14. 2025LightSNN: Lightweight Architecture Search for Sparse and Accurate Spiking Neural Networkscited by 1
  15. 2026Energy-Efficient Neuromorphic Computing for Edge AI: A Framework with Adaptive Spiking Neural Networks and Hardware-Aware Optimizationcited by 1
  16. 2022Training Stronger Spiking Neural Networks with Biomimetic Adaptive Internal Association Neuronscited by 0
  17. 2023SPENSER: Towards a NeuroEvolutionary Approach for Convolutional Spiking Neural Networkscited by 0
  18. 2024Training a General Spiking Neural Network with Improved Efficiency and Minimum Latencycited by 0
  19. 2024Toward Efficient Deep Spiking Neuron Networks:A Survey On Compressioncited by 0
  20. 2024Synaptic Modulation using Interspike Intervals Increases Energy Efficiency of Spiking Neural Networkscited by 0
  21. 2024Differentiable architecture search with multi-dimensional attention for spiking neural networkscited by 0
  22. 2024ILASH: A Predictive Neural Architecture Search Framework for Multi-Task Applicationscited by 0
  23. 2025Spiking Brain Compression: Post-Training Second-order Compression for Spiking Neural Networkscited by 0
  24. 2025SPEAR: Structured Pruning for Spiking Neural Networks via Synaptic Operation Estimation and Reinforcement Learningcited by 0
  25. 2026EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architecturescited by 0
  26. 2026NeuroTrain: Surveying Local Learning Rules for Spiking Neural Networks with an Open Benchmarking Frameworkcited by 0
  27. 2026LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networkscited by 0
  28. 2026CamoNAS: Neural Architecture Search for Enhanced Camouflaged Object Detectioncited by 0