mlsanityData to 7 Oct 2026

Optimizing the energy consumption of spiking neural networks for neuromorphic applications

arXiv 1912.01268 ↗First posted 3 Dec 2019cs.NE cs.LG q-bio.NCcited by 34

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Abstract (arXiv)

In the last few years, spiking neural networks have been demonstrated to perform on par with regular convolutional neural networks. Several works have proposed methods to convert a pre-trained CNN to a Spiking CNN without a significant sacrifice of performance. We demonstrate first that quantization-aware training of CNNs leads to better accuracy in SNNs. One of the benefits of converting CNNs to spiking CNNs is to leverage the sparse computation of SNNs and consequently perform equivalent computation at a lower energy consumption. Here we propose an efficient optimization strategy to train spiking networks at lower energy consumption, while maintaining similar accuracy levels. We demonstrate results on the MNIST-DVS and CIFAR-10 datasets.

Key facts. Cited by 34 papers in the map (data through 7 Oct 2026). First posted on arXiv 3 Dec 2019.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 6; 2022: 4; 2023: 7; 2024: 3; 2025: 7; 2026: 7.

34 papers.

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  1. 2023A Systematic Review of Green AIcited by 77
  2. 2023Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipelinecited by 50
  3. 2021Q-SpiNN: A Framework for Quantizing Spiking Neural Networkscited by 33
  4. 2023A Synthesis of Green Architectural Tactics for ML-Enabled Systemscited by 17
  5. 2021Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based Visioncited by 14
  6. 2022An Exact Mapping From ReLU Networks to Spiking Neural Networkscited by 12
  7. 2022Spiking neural networks for nonlinear regressioncited by 10
  8. 2021WaveSense: Efficient Temporal Convolutions with Spiking Neural Networks for Keyword Spottingcited by 8
  9. 2023Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principlescited by 8
  10. 2025QSViT: A Methodology for Quantizing Spiking Vision Transformerscited by 8
  11. 2021Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compressioncited by 7
  12. 2025Wandering around: A bioinspired approach to visual attention through object motion sensitivitycited by 5
22 more
  1. 2022Optimizing the Consumption of Spiking Neural Networks with Activity Regularizationcited by 4
  2. 2022enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networkscited by 4
  3. 2023Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networkscited by 3
  4. 2024Real-time Sub-milliwatt Epilepsy Detection Implemented on a Spiking Neural Network Edge Inference Processorcited by 3
  5. 2025Drone Detection Using a Low-Power Neuromorphic Virtual Tripwirecited by 2
  6. 2025Energy-Efficient and Dequantization-Free Q-LLMs: A Spiking Neural Network Approach to Salient Value Mitigationcited by 2
  7. 2021Optimizing for In-memory Deep Learning with Emerging Memory Technologycited by 1
  8. 2023The Hardware Impact of Quantization and Pruning for Weights in Spiking Neural Networkscited by 1
  9. 2025Region Masking to Accelerate Video Processing on Neuromorphic Hardwarecited by 1
  10. 2026An FPGA-Based SoC Architecture with a RISC-V Controller for Energy-Efficient Temporal-Coding Spiking Neural Networkscited by 1
  11. 2021Integrate-and-Fire Neurons for Low-Powered Pattern Recognitioncited by 0
  12. 2023Spiking Synaptic Penalty: Appropriate Penalty Term for Energy-Efficient Spiking Neural Networkscited by 0
  13. 2024A Theory of Synaptic Neural Balance: From Local to Global Ordercited by 0
  14. 2024On Reducing Activity with Distillation and Regularization for Energy Efficient Spiking Neural Networkscited by 0
  15. 2025Towards efficient keyword spotting using spike-based time difference encoderscited by 0
  16. 2025Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learningcited by 0
  17. 2026Line-based Event Preprocessing: Towards Low-Energy Neuromorphic Computer Visioncited by 0
  18. 2026PSViT: A Methodology for Structurally Pruning Spiking Vision Transformerscited by 0
  19. 2026PrimeSVT: An Automated Memory-aware Pruning Framework with Prioritized Compression Policy for Spiking Vision Transformerscited by 0
  20. 2026AQ4SViT: An Automated Quantization Framework with Search Gating Policy for Compressing Spiking Vision Transformerscited by 0
  21. 2026Adaptive Speech-to-Spike Encoding for Spiking Neural Networkscited by 0
  22. 2026Conjoint Audio-to-Spikes Encoding and Processing for Efficient Neuromorphic Speech Recognitioncited by 0