# CarSNN: An Efficient Spiking Neural Network for Event-Based Autonomous Cars on the Loihi Neuromorphic Research Processor

arXiv: https://arxiv.org/abs/2107.00401 · HTML: https://mlsanity.com/paper/2107.00401/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 51 references that are in the map.
- First posted on arXiv 1 Jul 2021.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Surrogate Gradient Learning in Spiking Neural Networks](https://mlsanity.com/paper/1901.09948/) (2019)
- [Efficient Processing of Deep Neural Networks: A Tutorial and Survey](https://mlsanity.com/paper/1703.09039/) (2017)
- [Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks](https://mlsanity.com/paper/1706.02609/) (2017)
- [Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccades](https://mlsanity.com/paper/1507.07629/) (2015)
- [SLAYER: Spike Layer Error Reassignment in Time](https://mlsanity.com/paper/1810.08646/) (2018)
- [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](https://mlsanity.com/paper/1803.07913/) (2018)
- [A Large Scale Event-based Detection Dataset for Automotive](https://mlsanity.com/paper/2001.08499/) (2020)
- [A Survey of Neuromorphic Computing and Neural Networks in Hardware](https://mlsanity.com/paper/1705.06963/) (2017)
- [DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction](https://mlsanity.com/paper/2005.08605/) (2020)
- [Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead](https://mlsanity.com/paper/2012.11233/) (2020)
- [FSpiNN: An Optimization Framework for Memory- and Energy-Efficient Spiking Neural Networks](https://mlsanity.com/paper/2007.08860/) (2020)
- [SpikeDyn: A Framework for Energy-Efficient Spiking Neural Networks with Continual and Unsupervised Learning Capabilities in Dynamic Environments](https://mlsanity.com/paper/2103.00424/) (2021)
- [An Efficient Spiking Neural Network for Recognizing Gestures with a DVS Camera on the Loihi Neuromorphic Processor](https://mlsanity.com/paper/2006.09985/) (2020)
- [SparkXD: A Framework for Resilient and Energy-Efficient Spiking Neural Network Inference using Approximate DRAM](https://mlsanity.com/paper/2103.00421/) (2021)
- [Online Few-shot Gesture Learning on a Neuromorphic Processor](https://mlsanity.com/paper/2008.01151/) (2020)
- [Spatiotemporal Filtering for Event-Based Action Recognition](https://mlsanity.com/paper/1903.07067/) (2019)

## Built on by (most cited first, 27 of 27)

- [Neuromorphic Data Augmentation for Training Spiking Neural Networks](https://mlsanity.com/paper/2203.06145/) (2022)
- [Emergent Visual Sensors for Autonomous Vehicles](https://mlsanity.com/paper/2205.09383/) (2022)
- [Sneaky Spikes: Uncovering Stealthy Backdoor Attacks in Spiking Neural Networks with Neuromorphic Data](https://mlsanity.com/paper/2302.06279/) (2023)
- [Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework](https://mlsanity.com/paper/2109.09829/) (2021)
- [Event-based Temporally Dense Optical Flow Estimation with Sequential Learning](https://mlsanity.com/paper/2210.01244/) (2022)
- [A Survey on Approximate Edge AI for Energy Efficient Autonomous Driving Services](https://mlsanity.com/paper/2304.14271/) (2023)
- [LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor](https://mlsanity.com/paper/2208.02253/) (2022)
- [An Efficient Knowledge Transfer Strategy for Spiking Neural Networks from Static to Event Domain](https://mlsanity.com/paper/2303.13077/) (2023)
- [Applications of Spiking Neural Networks in Visual Place Recognition](https://mlsanity.com/paper/2311.13186/) (2023)
- [Spiking Neural Network as Adaptive Event Stream Slicer](https://mlsanity.com/paper/2410.02249/) (2024)
- [Continual Learning with Neuromorphic Computing: Foundations, Methods, and Emerging Applications](https://mlsanity.com/paper/2410.09218/) (2024)
- [Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stack](https://mlsanity.com/paper/2404.03325/) (2024)
- [EvSegSNN: Neuromorphic Semantic Segmentation for Event Data](https://mlsanity.com/paper/2406.14178/) (2024)
- [Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems](https://mlsanity.com/paper/2204.09514/) (2022)
- [Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Network](https://mlsanity.com/paper/2412.16219/) (2024)
- [A Methodology to Study the Impact of Spiking Neural Network Parameters considering Event-Based Automotive Data](https://mlsanity.com/paper/2404.03493/) (2024)
- [SNN4Agents: A Framework for Developing Energy-Efficient Embodied Spiking Neural Networks for Autonomous Agents](https://mlsanity.com/paper/2404.09331/) (2024)
- [Pedestrian intention prediction in Adverse Weather Conditions with Spiking Neural Networks and Dynamic Vision Sensors](https://mlsanity.com/paper/2406.00473/) (2024)
- [FastSpiker: Enabling Fast Training for Spiking Neural Networks on Event-based Data through Learning Rate Enhancements for Autonomous Embedded Systems](https://mlsanity.com/paper/2407.05262/) (2024)
- [N-DriverMotion: Driver motion learning and prediction using an event-based camera and directly trained spiking neural networks on Loihi 2](https://mlsanity.com/paper/2408.13379/) (2024)
- [Unsupervised Backdoor Detection and Mitigation for Spiking Neural Networks](https://mlsanity.com/paper/2510.06629/) (2025)
- [Temporal Poisoning: Clean-Label Backdoors via Event Redistribution in SNNs](https://mlsanity.com/paper/2607.28075/) (2026)
- [Security-Aware Approximate Spiking Neural Networks](https://mlsanity.com/paper/2301.05264/) (2023)
- [Adaptive Calibration: A Unified Conversion Framework of Spiking Neural Networks](https://mlsanity.com/paper/2311.14265/) (2023)
- [Neuromorphic Computing for Embodied Intelligence in Autonomous Systems: Current Trends, Challenges, and Future Directions](https://mlsanity.com/paper/2507.18139/) (2025)
- [BadSNN: Backdoor Attacks on Spiking Neural Networks via Adversarial Spiking Neuron](https://mlsanity.com/paper/2602.07200/) (2026)
- [ETHEREAL: A 25.6-μs/inf. Low-latency Event-driven Graph-neural-network Processor for High-resolution Vision at the Edge](https://mlsanity.com/paper/2608.17787/) (2026)

## Abstract (arXiv)

Autonomous Driving (AD) related features provide new forms of mobility that are also beneficial for other kind of intelligent and autonomous systems like robots, smart transportation, and smart industries. For these applications, the decisions need to be made fast and in real-time. Moreover, in the quest for electric mobility, this task must follow low power policy, without affecting much the autonomy of the mean of transport or the robot. These two challenges can be tackled using the emerging Spiking Neural Networks (SNNs). When deployed on a specialized neuromorphic hardware, SNNs can achieve high performance with low latency and low power consumption. In this paper, we use an SNN connected to an event-based camera for facing one of the key problems for AD, i.e., the classification between cars and other objects. To consume less power than traditional frame-based cameras, we use a Dynamic Vision Sensor (DVS). The experiments are made following an offline supervised learning rule, followed by mapping the learnt SNN model on the Intel Loihi Neuromorphic Research Chip. Our best experiment achieves an accuracy on offline implementation of 86%, that drops to 83% when it is ported onto the Loihi Chip. The Neuromorphic Hardware implementation has maximum 0.72 ms of latency for every sample, and consumes only 310 mW. To the best of our knowledge, this work is the first implementation of an event-based car classifier on a Neuromorphic Chip.