ArticleFrontiers in neuroscience2020
Optimizing the Energy Consumption of Spiking Neural Networks for Neuromorphic Applications.
Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Weaving Intelligence: Thermally Drawn Multimaterial Fibers Toward AI-Enabled Smart Textiles.Advanced materials (Deerfield Beach, Fla.) · 2026Review
- Energy-efficient traffic sign recognition using directly trained spiking neural networks and population decoding.Frontiers in neuroscience · 2026Article
- A neuromorphic approach to early arrhythmia detection.Scientific reports · 2025Article
- Unsupervised post-training learning in spiking neural networks.Scientific reports · 2025Article
- A Comparative Study of Network-Based Machine Learning Approaches for Binary Classification in Metabolomics.Metabolites · 2025Article
- Event-driven figure-ground organisation model for the humanoid robot iCub.Nature communications · 2025Article
- Spiking neural networks for nonlinear regression.Royal Society open science · 2024Article
- Adversarial attacks on spiking convolutional neural networks for event-based vision.Frontiers in neuroscience · 2022Article
- A Sparsity-Driven Backpropagation-Less Learning Framework Using Populations of Spiking Growth Transform Neurons.Frontiers in neuroscience · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the last few years, spiking neural networks (SNNs) 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 optimization strategy to train efficient spiking networks with lower energy consumption, while maintaining similar accuracy levels. We demonstrate results on the MNIST-DVS and CIFAR-10 datasets.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.