# Detection of Fast-Moving Objects with Neuromorphic Hardware

arXiv: https://arxiv.org/abs/2403.10677 · HTML: https://mlsanity.com/paper/2403.10677/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 38 references that are in the map.
- First posted on arXiv 15 Mar 2024.

## Builds on (most cited first, 21 of 21)

- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [Training Spiking Neural Networks Using Lessons From Deep Learning](https://mlsanity.com/paper/2109.12894/) (2021)
- [Learning to Detect Objects with a 1 Megapixel Event Camera](https://mlsanity.com/paper/2009.13436/) (2020)
- [Long short-term memory and learning-to-learn in networks of spiking neurons](https://mlsanity.com/paper/1803.09574/) (2018)
- [Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection](https://mlsanity.com/paper/1903.06530/) (2019)
- [Event-based Moving Object Detection and Tracking](https://mlsanity.com/paper/1803.04523/) (2018)
- [An Analytical Estimation of Spiking Neural Networks Energy Efficiency](https://mlsanity.com/paper/2210.13107/) (2022)
- [Benchmarking Keyword Spotting Efficiency on Neuromorphic Hardware](https://mlsanity.com/paper/1812.01739/) (2018)
- [Event-based Agile Object Catching with a Quadrupedal Robot](https://mlsanity.com/paper/2303.17479/) (2023)
- [Robotic Table Tennis: A Case Study into a High Speed Learning System](https://mlsanity.com/paper/2309.03315/) (2023)
- [Balanced Resonate-and-Fire Neurons](https://mlsanity.com/paper/2402.14603/) (2024)
- [WaveSense: Efficient Temporal Convolutions with Spiking Neural Networks for Keyword Spotting](https://mlsanity.com/paper/2111.01456/) (2021)
- [GoalsEye: Learning High Speed Precision Table Tennis on a Physical Robot](https://mlsanity.com/paper/2210.03662/) (2022)
- [Linear Leaky-Integrate-and-Fire Neuron Model Based Spiking Neural Networks and Its Mapping Relationship to Deep Neural Networks](https://mlsanity.com/paper/2207.04889/) (2022)
- [eWand: A calibration framework for wide baseline frame-based and event-based camera systems](https://mlsanity.com/paper/2309.12685/) (2023)
- [Reliable Real Time Ball Tracking for Robot Table Tennis](https://mlsanity.com/paper/1908.07332/) (2019)
- [Exploiting Event Cameras for Spatio-Temporal Prediction of Fast-Changing Trajectories](https://mlsanity.com/paper/2001.01248/) (2020)
- [A multi-modal table tennis robot system](https://mlsanity.com/paper/2310.19062/) (2023)

## Built on by (most cited first, 4 of 4)

- [1000 Rallies: An Event-Camera Dataset and Real-Time Learned Ball-State Estimation for Robotic Table Tennis](https://mlsanity.com/paper/2606.25620/) (2026)
- [Robust PnP on a Neuromorphic Processor for Object Pose Estimation](https://mlsanity.com/paper/2607.16834/) (2026)
- [Neuromorphic Object Detection: An In-Depth Study and Future Directions](https://mlsanity.com/paper/2607.23576/) (2026)
- [Can Spiking Neural Networks play pinball? A neuromorphic motion detector for target tracking](https://mlsanity.com/paper/2609.24403/) (2026)

## Abstract (arXiv)

Neuromorphic Computing (NC) and Spiking Neural Networks (SNNs) in particular are often viewed as the next generation of Neural Networks (NNs). NC is a novel bio-inspired paradigm for energy efficient neural computation, often relying on SNNs in which neurons communicate via spikes in a sparse, event-based manner. This communication via spikes can be exploited by neuromorphic hardware implementations very effectively and results in a drastic reductions of power consumption and latency in contrast to regular GPU-based NNs. In recent years, neuromorphic hardware has become more accessible, and the support of learning frameworks has improved. However, available hardware is partially still experimental, and it is not transparent what these solutions are effectively capable of, how they integrate into real-world robotics applications, and how they realistically benefit energy efficiency and latency. In this work, we provide the robotics research community with an overview of what is possible with SNNs on neuromorphic hardware focusing on real-time processing. We introduce a benchmark of three popular neuromorphic hardware devices for the task of event-based object detection. Moreover, we show that an SNN on a neuromorphic hardware is able to run in a challenging table tennis robot setup in real-time.