用神经形态芯片实现超低功耗实时瞳孔追踪，适合可穿戴设备。
Realizing Fully-Integrated, Low-Power, Event-Based Pupil Tracking with Neuromorphic Hardware
- 基于事件视觉传感器与神经形态芯片，端到端实现低功耗瞳孔定位。
- 100赫兹追踪频率，单眼功耗低于5毫瓦，支持双目稳定运行。
- 首次完整集成的可穿戴系统，适合长期在线的智能眼镜等场景。
眼动追踪在众多应用中至关重要，但在可穿戴平台实现高频率、鲁棒且超低功耗的追踪仍具挑战。尽管事件视觉传感器具备微秒级分辨率和稀疏数据流，却缺乏完全集成、低功耗的实时推理处理方案。本文首次提出基于商用Speck2f系统级芯片的电池供电可穿戴瞳孔中心追踪系统，整合事件感知与神经形态计算，并在低功耗微控制器上实现轻量级坐标解码。系统采用新型带门控时间解码的不确定性量化脉冲神经网络，专为严格内存与带宽限制优化，辅以系统性部署机制以弥合现实差距。我们在新构建的多用户数据集上验证了该系统，展示出配备双神经形态设备的可穿戴原型可在100赫兹下实现鲁棒双目瞳孔追踪，每眼平均功耗低于5毫瓦。结果表明，端到端神经形态计算使下一代节能可穿戴系统的持续在线眼动追踪成为可能。
原文摘要 · Abstract (English)
Eye tracking is fundamental to numerous applications, yet achieving robust, high-frequency tracking with ultra-low power consumption remains challenging for wearable platforms. While event-based vision sensors offer microsecond resolution and sparse data streams, they have lacked fully integrated, low-power processing solutions capable of real-time inference. In this work, we present the first battery-powered, wearable pupil-center-tracking system with complete on-device integration, combining event-based sensing and neuromorphic processing on the commercially available Speck2f system-on-chip with lightweight coordinate decoding on a low-power microcontroller. Our solution features a novel uncertainty-quantifying spiking neural network with gated temporal decoding, optimized for strict memory and bandwidth constraints, complemented by systematic deployment mechanisms that bridge the reality gap. We validate our system on a new multi-user dataset and demonstrate a wearable prototype with dual neuromorphic devices achieving robust binocular pupil tracking at 100 Hz with an average power consumption below 5 mW per eye. Our work demonstrates that end-to-end neuromorphic computing enables practical, always-on eye tracking for next-generation energy-efficient wearable systems.
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