This paper introduces a neuromorphic methodology for eye tracking, harnessing
pure event data captured by a Dynamic Vision Sensor (DVS) camera. The framework
integrates a directly trained Spiking Neuron Network (SNN) regression model and
leverages a state-of-the-art low power edge neuromorphic processor - Speck,
collectively aiming to advance the precision and efficiency of eye-tracking
systems. First, we introduce a representative event-based eye-tracking dataset,
"Ini-30", which was collected with two glass-mounted DVS cameras from thirty
volunteers. Then,a SNN model, based on Integrate And Fire (IAF) neurons, named
"Retina", is described , featuring only 64k parameters (6.63x fewer than the
latest) and achieving pupil tracking error of only 3.24 pixels in a 64x64 DVS
input. The continous regression output is obtained by means of convolution
using a non-spiking temporal 1D filter slided across the output spiking layer.
Finally, we evaluate Retina on the neuromorphic processor, showing an
end-to-end power between 2.89-4.8 mW and a latency of 5.57-8.01 mS dependent on
the time window. We also benchmark our model against the latest event-based
eye-tracking method, "3ET", which was built upon event frames. Results show
that Retina achieves superior precision with 1.24px less pupil centroid error
and reduced computational complexity with 35 times fewer MAC operations. We
hope this work will open avenues for further investigation of close-loop
neuromorphic solutions and true event-based training pursuing edge performance.

本文介绍了一种用于眼动追踪的神经形态学方法，利用由 Dynamic Vision Sensor（DVS）摄像头捕获的纯事件数据。该方法结合了一个经过直接训练的脉冲神经网络回归模型，并利用了一种先进的低功耗边缘神经形态学处理器 Speck，旨在提高眼动追踪系统的精确度和效率。