  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.
