# Optimizing the energy consumption of spiking neural networks for neuromorphic applications

arXiv: https://arxiv.org/abs/1912.01268 · HTML: https://mlsanity.com/paper/1912.01268/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2019.

## Built on by (most cited first, 30 of 37)

- [A Systematic Review of Green AI](https://mlsanity.com/paper/2301.11047/) (2023)
- [Speck: A Smart event-based Vision Sensor with a low latency 327K Neuron Convolutional Neuronal Network Processing Pipeline](https://mlsanity.com/paper/2304.06793/) (2023)
- [Q-SpiNN: A Framework for Quantizing Spiking Neural Networks](https://mlsanity.com/paper/2107.01807/) (2021)
- [A Synthesis of Green Architectural Tactics for ML-Enabled Systems](https://mlsanity.com/paper/2312.09610/) (2023)
- [Adversarial Attacks on Spiking Convolutional Neural Networks for Event-based Vision](https://mlsanity.com/paper/2110.02929/) (2021)
- [An Exact Mapping From ReLU Networks to Spiking Neural Networks](https://mlsanity.com/paper/2212.12522/) (2022)
- [Energy-efficient Knowledge Distillation for Spiking Neural Networks](https://mlsanity.com/paper/2106.07172/) (2021)
- [Spiking neural networks for nonlinear regression](https://mlsanity.com/paper/2210.03515/) (2022)
- [WaveSense: Efficient Temporal Convolutions with Spiking Neural Networks for Keyword Spotting](https://mlsanity.com/paper/2111.01456/) (2021)
- [Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles](https://mlsanity.com/paper/2302.11618/) (2023)
- [QSViT: A Methodology for Quantizing Spiking Vision Transformers](https://mlsanity.com/paper/2504.00948/) (2025)
- [Towards Low-Latency Energy-Efficient Deep SNNs via Attention-Guided Compression](https://mlsanity.com/paper/2107.12445/) (2021)
- [Wandering around: A bioinspired approach to visual attention through object motion sensitivity](https://mlsanity.com/paper/2502.06747/) (2025)
- [Optimizing the Consumption of Spiking Neural Networks with Activity Regularization](https://mlsanity.com/paper/2204.01460/) (2022)
- [enpheeph: A Fault Injection Framework for Spiking and Compressed Deep Neural Networks](https://mlsanity.com/paper/2208.00328/) (2022)
- [Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks](https://mlsanity.com/paper/2308.04672/) (2023)
- [Real-time Sub-milliwatt Epilepsy Detection Implemented on a Spiking Neural Network Edge Inference Processor](https://mlsanity.com/paper/2410.16613/) (2024)
- [Drone Detection Using a Low-Power Neuromorphic Virtual Tripwire](https://mlsanity.com/paper/2509.12997/) (2025)
- [Energy-Efficient and Dequantization-Free Q-LLMs: A Spiking Neural Network Approach to Salient Value Mitigation](https://mlsanity.com/paper/2510.19498/) (2025)
- [Optimizing for In-memory Deep Learning with Emerging Memory Technology](https://mlsanity.com/paper/2112.00324/) (2021)
- [The Hardware Impact of Quantization and Pruning for Weights in Spiking Neural Networks](https://mlsanity.com/paper/2302.04174/) (2023)
- [Region Masking to Accelerate Video Processing on Neuromorphic Hardware](https://mlsanity.com/paper/2503.16775/) (2025)
- [An FPGA-Based SoC Architecture with a RISC-V Controller for Energy-Efficient Temporal-Coding Spiking Neural Networks](https://mlsanity.com/paper/2603.18054/) (2026)
- [Integrate-and-Fire Neurons for Low-Powered Pattern Recognition](https://mlsanity.com/paper/2106.14596/) (2021)
- [Spiking Synaptic Penalty: Appropriate Penalty Term for Energy-Efficient Spiking Neural Networks](https://mlsanity.com/paper/2302.01500/) (2023)
- [A Theory of Synaptic Neural Balance: From Local to Global Order](https://mlsanity.com/paper/2405.09688/) (2024)
- [On Reducing Activity with Distillation and Regularization for Energy Efficient Spiking Neural Networks](https://mlsanity.com/paper/2406.18350/) (2024)
- [Towards efficient keyword spotting using spike-based time difference encoders](https://mlsanity.com/paper/2503.15402/) (2025)
- [Self-cross Feature based Spiking Neural Networks for Efficient Few-shot Learning](https://mlsanity.com/paper/2505.07921/) (2025)
- [NeuRehab: A Reinforcement Learning and Spiking Neural Network-Based Rehab Automation Framework](https://mlsanity.com/paper/2512.17841/) (2025)

## 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.