实现基于神经形态硬件的低功耗全集成事件式瞳孔追踪
Realizing Fully-Integrated, Low-Power, Event-Based Pupil Tracking with Neuromorphic Hardware
AI总结：
本研究提出了一种基于神经形态硬件的低功耗可穿戴瞳孔追踪系统，实现了100 Hz的高频率追踪，平均功耗低于5 mW，展示了端到端神经形态计算在可穿戴设备中的应用潜力。
摘要：
眼动追踪是众多应用的基础，但实现鲁棒、高频追踪的同时保持超低功耗仍对可穿戴平台构成挑战。尽管基于事件的视觉传感器提供微秒级分辨率和稀疏数据流，但缺乏能够实现实时推断的完全集成、低功耗处理解决方案。在本工作中，我们提出了首个电池供电、可穿戴的瞳孔中心追踪系统，结合了基于事件的传感和神经形态处理，利用商用的Speck2f系统芯片和轻量级坐标解码的低功耗微控制器。我们的解决方案包含一种新的不确定性量化脉冲神经网络，具有门控时间解码，优化以适应严格的内存和带宽限制，并辅以系统部署机制以弥合现实差距。我们在新的多用户数据集上验证了系统，并展示了一个双神经形态设备的可穿戴原型，实现了100 Hz的稳健双眼瞳孔追踪，平均功耗低于5 mW每只眼。我们的工作证明了端到端的神经形态计算能够为下一代节能可穿戴系统实现实用的持续眼动追踪。
英文摘要：
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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