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

arXiv: https://arxiv.org/abs/2104.10719 · HTML: https://mlsanity.com/paper/2104.10719/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 60 references that are in the map.
- First posted on arXiv 21 Apr 2021.

## Builds on (most cited first, 26 of 26)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://mlsanity.com/paper/1806.07572/) (2018)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition](https://mlsanity.com/paper/1310.1531/) (2013)
- [Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks](https://mlsanity.com/paper/1706.02609/) (2017)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks](https://mlsanity.com/paper/1901.06053/) (2019)
- [Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection](https://mlsanity.com/paper/1903.06530/) (2019)
- [Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation](https://mlsanity.com/paper/2005.01807/) (2020)
- [The Heavy-Tail Phenomenon in SGD](https://mlsanity.com/paper/2006.04740/) (2020)
- [Enabling Spike-based Backpropagation for Training Deep Neural Network Architectures](https://mlsanity.com/paper/1903.06379/) (2019)
- [Dropout Sampling for Robust Object Detection in Open-Set Conditions](https://mlsanity.com/paper/1710.06677/) (2017)
- [Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks](https://mlsanity.com/paper/2006.09313/) (2020)
- [Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? -- A Neural Tangent Kernel Perspective](https://mlsanity.com/paper/2002.06262/) (2020)
- [Evaluating Merging Strategies for Sampling-based Uncertainty Techniques in Object Detection](https://mlsanity.com/paper/1809.06006/) (2018)
- [Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware](https://mlsanity.com/paper/1601.04187/) (2016)
- [What makes for effective detection proposals?](https://mlsanity.com/paper/1502.05082/) (2015)
- [Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks](https://mlsanity.com/paper/1612.04052/) (2016)
- [Bio-Inspired Spiking Convolutional Neural Network using Layer-wise Sparse Coding and STDP Learning](https://mlsanity.com/paper/1611.03000/) (2016)
- [What leads to generalization of object proposals?](https://mlsanity.com/paper/2008.05700/) (2020)
- [Towards Scalable, Efficient and Accurate Deep Spiking Neural Networks with Backward Residual Connections, Stochastic Softmax and Hybridization](https://mlsanity.com/paper/1910.13931/) (2019)

## Built on by (most cited first, 30 of 31)

- [Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time](https://mlsanity.com/paper/2112.11231/) (2021)
- [Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networks](https://mlsanity.com/paper/2110.08858/) (2021)
- [Spike Calibration: Fast and Accurate Conversion of Spiking Neural Network for Object Detection and Segmentation](https://mlsanity.com/paper/2207.02702/) (2022)
- [Spiking Neural Network for Ultra-low-latency and High-accurate Object Detection](https://mlsanity.com/paper/2306.12010/) (2023)
- [STAA-SNN: Spatial-Temporal Attention Aggregator for Spiking Neural Networks](https://mlsanity.com/paper/2503.02689/) (2025)
- [To Spike or Not To Spike: A Digital Hardware Perspective on Deep Learning Acceleration](https://mlsanity.com/paper/2306.15749/) (2023)
- [Sparse Spiking Neural Network: Exploiting Heterogeneity in Timescales for Pruning Recurrent SNN](https://mlsanity.com/paper/2403.03409/) (2024)
- [Spiking Tucker Fusion Transformer for Audio-Visual Zero-Shot Learning](https://mlsanity.com/paper/2407.08130/) (2024)
- [Heterogeneous Neuronal and Synaptic Dynamics for Spike-Efficient Unsupervised Learning: Theory and Design Principles](https://mlsanity.com/paper/2302.11618/) (2023)
- [Spiking-Diffusion: Vector Quantized Discrete Diffusion Model with Spiking Neural Networks](https://mlsanity.com/paper/2308.10187/) (2023)
- [Adaptive Reorganization of Neural Pathways for Continual Learning with Spiking Neural Networks](https://mlsanity.com/paper/2309.09550/) (2023)
- [Frequency-Adaptive Low-Latency Object Detection Using Events and Frames](https://mlsanity.com/paper/2412.04149/) (2024)
- [SU-YOLO: Spiking Neural Network for Efficient Underwater Object Detection](https://mlsanity.com/paper/2503.24389/) (2025)
- [Developmental Plasticity-inspired Adaptive Pruning for Deep Spiking and Artificial Neural Networks](https://mlsanity.com/paper/2211.12714/) (2022)
- [Brain-Inspired Spiking Neural Network for Online Unsupervised Time Series Prediction](https://mlsanity.com/paper/2304.04697/) (2023)
- [Deep Multi-Threshold Spiking-UNet for Image Processing](https://mlsanity.com/paper/2307.10974/) (2023)
- [TS-SNN: Temporal Shift Module for Spiking Neural Networks](https://mlsanity.com/paper/2505.04165/) (2025)
- [Topological Representations of Heterogeneous Learning Dynamics of Recurrent Spiking Neural Networks](https://mlsanity.com/paper/2403.12462/) (2024)
- [Detecting Every Object from Events](https://mlsanity.com/paper/2404.05285/) (2024)
- [Underwater Image Enhancement by Convolutional Spiking Neural Networks](https://mlsanity.com/paper/2503.20485/) (2025)
- [ESVAE: An Efficient Spiking Variational Autoencoder with Reparameterizable Poisson Spiking Sampling](https://mlsanity.com/paper/2310.14839/) (2023)
- [Exploiting Heterogeneity in Timescales for Sparse Recurrent Spiking Neural Networks for Energy-Efficient Edge Computing](https://mlsanity.com/paper/2407.06452/) (2024)
- [Similarity-based context aware continual learning for spiking neural networks](https://mlsanity.com/paper/2411.05802/) (2024)
- [A Dynamical Systems-Inspired Pruning Strategy for Addressing Oversmoothing in Graph Neural Networks](https://mlsanity.com/paper/2412.07243/) (2024)
- [Continual Learning of Multiple Cognitive Functions with Brain-inspired Temporal Development Mechanism](https://mlsanity.com/paper/2504.05621/) (2025)
- [Low Latency of object detection for spikng neural network](https://mlsanity.com/paper/2309.15555/) (2023)
- [Energy-Efficient Visual Search by Eye Movement and Low-Latency Spiking Neural Network](https://mlsanity.com/paper/2310.06578/) (2023)
- [Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets through Strongly Scattering Media](https://mlsanity.com/paper/2501.03874/) (2025)
- [snnTrans-DHZ: A Lightweight Spiking Neural Network Architecture for Underwater Image Dehazing](https://mlsanity.com/paper/2504.11482/) (2025)
- [Neuromorphic Object Detection: An In-Depth Study and Future Directions](https://mlsanity.com/paper/2607.23576/) (2026)

## 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.