在微控制器上实现亚毫秒级眼动追踪，低功耗实时运行。
Sub-Millisecond Event-Based Eye Tracking on a Resource-Constrained Microcontroller
- 基于事件视觉传感器与轻量CNN，在嵌入式芯片上实现实时推理。
- 端到端延迟仅385微秒，误差低于6像素，能耗仅155微焦。
- 适合智能眼镜等可穿戴设备，兼顾低延迟与超低功耗。
本文提出一种部署于资源受限微控制器上的新型事件基眼动追踪系统，解决嵌入式系统中实时性、低延迟和低功耗的挑战。系统采用动态视觉传感器（DVS）DVXplorer Micro，平均时间分辨率200 μs，捕捉快速眼动具有极低延迟。硬件平台为STMicroelectronics新型低功耗高性能微控制器STM32N6，配备800 MHz Arm Cortex-M55核心与神经网络加速器Neural-ART，支持毫瓦级功耗下的实时推理。论文设计了一种面向事件数据的轻量化卷积神经网络（CNN），在边缘侧部署，于Ini-30数据集上实现均值瞳孔预测误差5.99像素，中位误差5.73像素。系统端到端推理延迟仅为385 μs，神经网络每周期完成52次乘加运算（MAC），能耗仅155 μJ。该方案实现了全嵌入式、高能效的眼动追踪，适用于智能眼镜等可穿戴设备。
原文摘要 · Abstract (English)
This paper presents a novel event-based eye-tracking system deployed on a resource-constrained microcontroller, addressing the challenges of real-time, low-latency, and low-power performance in embedded systems. The system leverages a Dynamic Vision Sensor (DVS), specifically the DVXplorer Micro, with an average temporal resolution of 200 μs, to capture rapid eye movements with extremely low latency. The system is implemented on a novel low-power and high-performance microcontroller from STMicroelectronics, the STM32N6. The microcontroller features an 800 MHz Arm Cortex-M55 core and AI hardware accelerator, the Neural-ART Accelerator, enabling real-time inference with milliwatt power consumption. The paper propose a hardware-aware and sensor-aware compact Convolutional Neuron Network (CNN) optimized for event-based data, deployed at the edge, achieving a mean pupil prediction error of 5.99 pixels and a median error of 5.73 pixels on the Ini-30 dataset. The system achieves an end-to-end inference latency of just 385 μs and a neural network throughput of 52 Multiply and Accumulate (MAC) operations per cycle while consuming just 155 μJ of energy. This approach allows for the development of a fully embedded, energy-efficient eye-tracking solution suitable for applications such as smart glasses and wearable devices.
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