# Spiking Optical Flow for Event-based Sensors Using IBM's TrueNorth Neurosynaptic System

arXiv: https://arxiv.org/abs/1710.09820 · HTML: https://mlsanity.com/paper/1710.09820/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2017.

## Built on by (most cited first, 14 of 14)

- [Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks](https://mlsanity.com/paper/2106.01862/) (2021)
- [Deep Learning for Event-based Vision: A Comprehensive Survey and Benchmarks](https://mlsanity.com/paper/2302.08890/) (2023)
- [Event-based Video Reconstruction via Potential-assisted Spiking Neural Network](https://mlsanity.com/paper/2201.10943/) (2022)
- [Spatio-Temporal Recurrent Networks for Event-Based Optical Flow Estimation](https://mlsanity.com/paper/2109.04871/) (2021)
- [SpikeMS: Deep Spiking Neural Network for Motion Segmentation](https://mlsanity.com/paper/2105.06562/) (2021)
- [Fast Event-based Optical Flow Estimation by Triplet Matching](https://mlsanity.com/paper/2212.12218/) (2022)
- [Globally Optimal Event-Based Divergence Estimation for Ventral Landing](https://mlsanity.com/paper/2209.13168/) (2022)
- [Fusing Frame and Event Vision for High-speed Optical Flow for Edge Application](https://mlsanity.com/paper/2207.10720/) (2022)
- [TOFFE -- Temporally-binned Object Flow from Events for High-speed and Energy-Efficient Object Detection and Tracking](https://mlsanity.com/paper/2501.12482/) (2025)
- [Adversarial Defense via Neural Oscillation inspired Gradient Masking](https://mlsanity.com/paper/2211.02223/) (2022)
- [Advanced Computing and Related Applications Leveraging Brain-inspired Spiking Neural Networks](https://mlsanity.com/paper/2309.04426/) (2023)
- [ST-FlowNet: An Efficient Spiking Neural Network for Event-Based Optical Flow Estimation](https://mlsanity.com/paper/2503.10195/) (2025)
- [EventShiftFlow: Towards Hardware-efficient FPGA-based Flow Estimation](https://mlsanity.com/paper/2605.28312/) (2026)
- [Probing Geospatial SSL Representations with Environmental Signals](https://mlsanity.com/paper/2607.05207/) (2026)

## Abstract (arXiv)

This paper describes a fully spike-based neural network for optical flow estimation from Dynamic Vision Sensor data. A low power embedded implementation of the method which combines the Asynchronous Time-based Image Sensor with IBM's TrueNorth Neurosynaptic System is presented. The sensor generates spikes with sub-millisecond resolution in response to scene illumination changes. These spike are processed by a spiking neural network running on TrueNorth with a 1 millisecond resolution to accurately determine the order and time difference of spikes from neighboring pixels, and therefore infer the velocity. The spiking neural network is a variant of the Barlow Levick method for optical flow estimation. The system is evaluated on two recordings for which ground truth motion is available, and achieves an Average Endpoint Error of 11% at an estimated power budget of under 80mW for the sensor and computation.