# An Analytical Estimation of Spiking Neural Networks Energy Efficiency

arXiv: https://arxiv.org/abs/2210.13107 · HTML: https://mlsanity.com/paper/2210.13107/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2022.

## Built on by (most cited first, 30 of 38)

- [SpikingJelly: An open-source machine learning infrastructure platform for spike-based intelligence](https://mlsanity.com/paper/2310.16620/) (2023)
- [NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems](https://mlsanity.com/paper/2304.04640/) (2023)
- [ESL-SNNs: An Evolutionary Structure Learning Strategy for Spiking Neural Networks](https://mlsanity.com/paper/2306.03693/) (2023)
- [Learning Long Sequences in Spiking Neural Networks](https://mlsanity.com/paper/2401.00955/) (2023)
- [Training Machine Learning models at the Edge: A Survey](https://mlsanity.com/paper/2403.02619/) (2024)
- [Spiking CenterNet: A Distillation-boosted Spiking Neural Network for Object Detection](https://mlsanity.com/paper/2402.01287/) (2024)
- [Neuroscience inspired scientific machine learning (Part-1): Variable spiking neuron for regression](https://mlsanity.com/paper/2311.09267/) (2023)
- [Deep Multi-Threshold Spiking-UNet for Image Processing](https://mlsanity.com/paper/2307.10974/) (2023)
- [Detection of Fast-Moving Objects with Neuromorphic Hardware](https://mlsanity.com/paper/2403.10677/) (2024)
- [Time to Spike? Understanding the Representational Power of Spiking Neural Networks in Discrete Time](https://mlsanity.com/paper/2505.18023/) (2025)
- [Spiking monocular event based 6D pose estimation for space application](https://mlsanity.com/paper/2501.02916/) (2025)
- [Underwater Image Enhancement by Convolutional Spiking Neural Networks](https://mlsanity.com/paper/2503.20485/) (2025)
- [Event-Driven Neuromorphic Vision Enables Energy-Efficient Visual Place Recognition](https://mlsanity.com/paper/2604.03277/) (2026)
- [Neuromorphic Event-Driven Semantic Communication in Microgrids](https://mlsanity.com/paper/2402.18390/) (2024)
- [A Novel Spike Transformer Network for Depth Estimation from Event Cameras via Cross-modality Knowledge Distillation](https://mlsanity.com/paper/2404.17335/) (2024)
- [Embedded event based object detection with spiking neural network](https://mlsanity.com/paper/2406.17617/) (2024)
- [Enabling Energy-Efficient Simultaneous Multi-Task Reinforcement Learning through Spiking Neural Networks with Active Dendrites for Bio-inspired Generalist Agents](https://mlsanity.com/paper/2412.04847/) (2024)
- [An Inherent Trade-Off in Noisy Neural Communication with Rank-Order Coding](https://mlsanity.com/paper/2308.07034/) (2023)
- [Neuroscience inspired scientific machine learning (Part-2): Variable spiking wavelet neural operator](https://mlsanity.com/paper/2311.14710/) (2023)
- [A Cloud-Edge Framework for Energy-Efficient Event-Driven Control: An Integration of Online Supervised Learning, Spiking Neural Networks and Local Plasticity Rules](https://mlsanity.com/paper/2405.02316/) (2024)
- [Enhanced Neuromorphic Semantic Segmentation Latency through Stream Event](https://mlsanity.com/paper/2502.18982/) (2025)
- [FFGAF-SNN: The Forward-Forward Based Gradient Approximation Free Training Framework for Spiking Neural Networks](https://mlsanity.com/paper/2507.23643/) (2025)
- [Hardware-aware vs. Hardware-agnostic Energy Estimation for SNN in Space Applications](https://mlsanity.com/paper/2508.19654/) (2025)
- [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)
- [All in one timestep: Enhancing Sparsity and Energy efficiency in Multi-level Spiking Neural Networks](https://mlsanity.com/paper/2510.24637/) (2025)
- [Line-based Event Preprocessing: Towards Low-Energy Neuromorphic Computer Vision](https://mlsanity.com/paper/2601.10742/) (2026)
- [UltraLIF: Fully Differentiable Spiking Neural Networks via Ultradiscretization and Max-Plus Algebra](https://mlsanity.com/paper/2602.11206/) (2026)
- [Federated Learning of Spiking Neural Networks under Heterogeneous Temporal Resolutions](https://mlsanity.com/paper/2605.15355/) (2026)
- [SpikeReg: Energy-Efficient 3D Deformable Medical Image Registration with Spiking Neural Networks](https://mlsanity.com/paper/2605.25144/) (2026)

## Abstract (arXiv)

Spiking Neural Networks are a type of neural networks where neurons communicate using only spikes. They are often presented as a low-power alternative to classical neural networks, but few works have proven these claims to be true. In this work, we present a metric to estimate the energy consumption of SNNs independently of a specific hardware. We then apply this metric on SNNs processing three different data types (static, dynamic and event-based) representative of real-world applications. As a result, all of our SNNs are 6 to 8 times more efficient than their FNN counterparts.