Intelligent edge vision tasks encounter the critical challenge of ensuring power and latency efficiency due to the typically heavy computational load they impose on edge platforms.This work leverages one of the first "AI in sensor" vision platforms, IMX500 by Sony, to achieve ultra-fast and ultra-low-power end-to-end edge vision applications. We evaluate the IMX500 and compare it to other edge platforms, such as the Google Coral Dev Micro and Sony Spresense, by exploring gaze estimation as a case study. We propose TinyTracker, a highly efficient, fully quantized model for 2D gaze estimation designed to maximize the performance of the edge vision systems considered in this study. TinyTracker achieves a 41x size reduction (600Kb) compared to iTracker [1] without significant loss in gaze estimation accuracy (maximum of 0.16 cm when fully quantized). TinyTracker's deployment on the Sony IMX500 vision sensor results in end-to-end latency of around 19ms. The camera takes around 17.9ms to read, process and transmit the pixels to the accelerator. The inference time of the network is 0.86ms with an additional 0.24 ms for retrieving the results from the sensor. The overall energy consumption of the end-to-end system is 4.9 mJ, including 0.06 mJ for inference. The end-to-end study shows that IMX500 is 1.7x faster than CoralMicro (19ms vs 34.4ms) and 7x more power efficient (4.9mJ VS 34.2mJ)

翻译：智能边缘视觉任务因通常在边缘平台上施加繁重的计算负载，而面临确保功耗与延迟效率的关键挑战。本研究利用索尼IMX500这一首批"传感器内人工智能"视觉平台，实现极快超低功耗的端到端边缘视觉应用。我们以视线估计为案例研究，评估IMX500并与其他边缘平台（如Google Coral Dev Micro和Sony Spresense）进行对比。我们提出TinyTracker，一种面向二维视线估计的高效全量化模型，旨在最大化本研究所考虑的边缘视觉系统的性能。与iTracker [1]相比，TinyTracker实现了41倍的模型大小缩减（600Kb），而视线估计精度无显著损失（全量化时最大误差为0.16厘米）。TinyTracker在索尼IMX500视觉传感器上的部署可实现约19ms的端到端延迟。摄像头读取、处理并将像素传输至加速器耗时约17.9ms。网络推理时间为0.86ms，从传感器获取结果额外耗时0.24ms。端到端系统的总能耗为4.9 mJ，其中推理能耗为0.06 mJ。端到端研究表明，IMX500比CoralMicro快1.7倍（19ms对比34.4ms），且能效高出7倍（4.9mJ对比34.2mJ）。