mlsanityData to 7 Oct 2026

EventF2S: Asynchronous and Sparse Spiking AER Framework using Neuromorphic-Friendly Algorithm

arXiv 2402.10078 ↗First posted 28 Jan 2024cs.NE cs.LG eess.SPcited by 0

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Abstract (arXiv)

Bio-inspired Address Event Representation (AER) sensors have attracted significant popularity owing to their low power consumption, high sparsity, and high temporal resolution. Spiking Neural Network (SNN) has become the inherent choice for AER data processing. However, the integration of the AER-SNN paradigm has not adequately explored asynchronous processing, neuromorphic compatibility, and sparse spiking, which are the key requirements of resource-constrained applications. To address this gap, we introduce a brain-inspired AER-SNN object recognition solution, which includes a data encoder integrated with a First-To-Spike recognition network. Being fascinated by the functionality of neurons in the visual cortex, we designed the solution to be asynchronous and compatible with neuromorphic hardware. Furthermore, we have adapted the principle of denoising and First-To-Spike coding to achieve optimal spike signaling, significantly reducing computation costs. Experimental evaluation has demonstrated that the proposed method incurs significantly less computation cost to achieve state-of-the-art competitive accuracy. Overall, the proposed solution offers an asynchronous and cost-effective AER recognition system that harnesses the full potential of AER sensors.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 17 of its 37 references that are in the map. First posted on arXiv 28 Jan 2024.

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17 of 37 references.

References · 17

  1. 2019Event-based Vision: A Surveycited by 1,010
  2. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
  3. 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 308
  4. 2018Direct Training for Spiking Neural Networks: Faster, Larger, Bettercited by 222
  5. 2018HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classificationcited by 215
  6. 2018Event-based Vision meets Deep Learning on Steering Prediction for Self-driving Carscited by 197
  7. 2018Long short-term memory and learning-to-learn in networks of spiking neuronscited by 179
  8. 2020Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagationcited by 142
  9. 2020Spike-FlowNet: Event-based Optical Flow Estimation with Energy-Efficient Hybrid Neural Networkscited by 98
  10. 2016Supervised learning based on temporal coding in spiking neural networkscited by 75
  11. 2015HFirst: A Temporal Approach to Object Recognitioncited by 50
  12. 2017Gradient Descent for Spiking Neural Networkscited by 47
5 more
  1. 2019Temporal-Coded Deep Spiking Neural Network with Easy Training and Robust Performancecited by 45
  2. 2020Convolutional Spiking Neural Networks for Spatio-Temporal Feature Extractioncited by 23
  3. 2020Effective AER Object Classification Using Segmented Probability-Maximization Learning in Spiking Neural Networkscited by 16
  4. 2019Unsupervised AER Object Recognition Based on Multiscale Spatio-Temporal Features and Spiking Neuronscited by 14
  5. 2021A Spike Learning System for Event-driven Object Recognitioncited by 2

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