mlsanityData to 7 Oct 2026

A Fully Spiking Hybrid Neural Network for Energy-Efficient Object Detection

arXiv 2104.10719 ↗First posted 21 Apr 2021cs.CV cs.AI eess.IVcited by 31

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Abstract (arXiv)

This paper proposes a Fully Spiking Hybrid Neural Network (FSHNN) for energy-efficient and robust object detection in resource-constrained platforms. The network architecture is based on Convolutional SNN using leaky-integrate-fire neuron models. The model combines unsupervised Spike Time-Dependent Plasticity (STDP) learning with back-propagation (STBP) learning methods and also uses Monte Carlo Dropout to get an estimate of the uncertainty error. FSHNN provides better accuracy compared to DNN based object detectors while being 150X energy-efficient. It also outperforms these object detectors, when subjected to noisy input data and less labeled training data with a lower uncertainty error.

Key facts. Cited by 31 papers in the map (data through 7 Oct 2026). Builds on 26 of its 60 references that are in the map. First posted on arXiv 21 Apr 2021.

Builds on

26 of 60 references.

References · 26

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2015Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learningcited by 4,416
  3. 2015SSD: Single Shot MultiBox Detectorcited by 3,125
  4. 2018YOLOv3: An Incremental Improvementcited by 2,652
  5. 2018Neural Tangent Kernel: Convergence and Generalization in Neural Networkscited by 2,597
  6. 2013Visualizing and Understanding Convolutional Networkscited by 2,278
  7. 2014How transferable are features in deep neural networks?cited by 1,216
  8. 2013DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognitioncited by 422
  9. 2017Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networkscited by 399
  10. 2018Deep Learning in Spiking Neural Networkscited by 258
  11. 2018Going Deeper in Spiking Neural Networks: VGG and Residual Architecturescited by 236
  12. 2019A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networkscited by 213
14 more
  1. 2019Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detectioncited by 174
  2. 2020Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagationcited by 142
  3. 2020The Heavy-Tail Phenomenon in SGDcited by 122
  4. 2019Enabling Spike-based Backpropagation for Training Deep Neural Network Architecturescited by 119
  5. 2017Dropout Sampling for Robust Object Detection in Open-Set Conditionscited by 112
  6. 2020Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networkscited by 62
  7. 2020Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? -- A Neural Tangent Kernel Perspectivecited by 60
  8. 2018Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detectioncited by 51
  9. 2016Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardwarecited by 39
  10. 2015What makes for effective detection proposals?cited by 38
  11. 2016Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networkscited by 31
  12. 2016Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learningcited by 9
  13. 2020What leads to generalization of object proposals?cited by 9
  14. 2019Towards Scalable, Efficient and Accurate Deep Spiking Neural Networks with Backward Residual Connections, Stochastic Softmax and Hybridizationcited by 4

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 2; 2022: 2; 2023: 10; 2024: 8; 2025: 7; 2026: 2.

31 papers.

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  1. 2021Accurate online training of dynamical spiking neural networks through Forward Propagation Through Timecited by 33
  2. 2021Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networkscited by 22
  3. 2022Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentationcited by 15
  4. 2023Spiking Neural Network for Ultra-low-latency and High-accurate Object Detectioncited by 14
  5. 2025STAA-SNN: Spatial-Temporal Attention Aggregator for Spiking Neural Networkscited by 11
  6. 2023To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Accelerationcited by 10
  7. 2024Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNNcited by 10
  8. 2024Spiking Tucker Fusion Transformer for Audio-Visual Zero-Shot Learningcited by 10
  9. 2023Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principlescited by 8
  10. 2023Spiking-Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networkscited by 7
  11. 2023Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networkscited by 7
  12. 2024Frequency-Adaptive Low-Latency Object Detection Using Events and Framescited by 7
19 more
  1. 2025SU-YOLO: Spiking Neural Network for Efficient Underwater Object Detectioncited by 7
  2. 2022Developmental Plasticity-inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networkscited by 6
  3. 2023Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Predictioncited by 5
  4. 2023Deep Multi-Threshold Spiking-UNet for Image Processingcited by 5
  5. 2025TS-SNN: Temporal Shift Module for Spiking Neural Networkscited by 5
  6. 2024Topological Representations of Heterogeneous Learning Dynamics of Recurrent Spiking Neural Networkscited by 3
  7. 2024Detecting Every Object from Eventscited by 3
  8. 2025Underwater Image Enhancement by Convolutional Spiking Neural Networkscited by 3
  9. 2023ESVAE: An Efficient Spiking Variational Autoencoder with Reparameterizable Poisson Spiking Samplingcited by 2
  10. 2024Exploiting Heterogeneity in Timescales for Sparse Recurrent Spiking Neural Networks for Energy-Efficient Edge Computingcited by 2
  11. 2024Similarity-based context aware continual learning for spiking neural networkscited by 1
  12. 2024A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networkscited by 1
  13. 2025Continual Learning of Multiple Cognitive Functions with Brain-inspired Temporal Development Mechanismcited by 1
  14. 2023Low Latency of object detection for spikng neural networkcited by 0
  15. 2023Energy-Efficient Visual Search by Eye Movement and Low-Latency Spiking Neural Networkcited by 0
  16. 2025Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets through Strongly Scattering Mediacited by 0
  17. 2025snnTrans-DHZ: A Lightweight Spiking Neural Network Architecture for Underwater Image Dehazingcited by 0
  18. 2026Neuromorphic Object Detection: An In-Depth Study and Future Directionscited by 0
  19. 2026Spiking Neural Networks for Energy-Efficient Object Detection in Forward-Looking Sonar Imagerycited by 0