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.

翻译：本文提出了一种基于神经形态的眼动追踪方法，利用动态视觉传感器（DVS）相机捕获的纯事件数据。该框架集成了直接训练的脉冲神经网络（SNN）回归模型，并采用了最先进的低功耗边缘神经形态处理器Speck，旨在提升眼动追踪系统的精度和效率。首先，我们介绍了一个代表性的事件驱动眼动追踪数据集“Ini-30”，该数据集使用两个安装在眼镜上的DVS相机从三十名志愿者处采集。然后，描述了基于积分点火（IAF）神经元的SNN模型“Retina”，该模型仅包含64k个参数（比最新模型少6.63倍），在64x64 DVS输入中实现了仅3.24像素的瞳孔追踪误差。通过使用非脉冲时间一维滤波器在输出脉冲层上滑动进行卷积，获得连续回归输出。最后，我们在神经形态处理器上评估了Retina，展示了端到端功耗在2.89-4.8 mW之间，延迟在5.57-8.01 ms之间（取决于时间窗口）。我们还将模型与最新的事件驱动眼动追踪方法“3ET”（基于事件帧构建）进行了基准测试。结果表明，Retina实现了更高的精度（瞳孔中心误差减少1.24像素）和更低的计算复杂度（MAC操作减少35倍）。我们希望这项工作能为闭环神经形态解决方案以及追求边缘性能的真实事件驱动训练的进一步研究开辟新途径。