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：智能边缘视觉任务面临关键挑战，即由于其在边缘平台上通常承受繁重的计算负载，需确保功耗和延迟效率。本研究利用索尼公司首批“人工智能传感器内”视觉平台之一IMX500，实现超快且超低功耗的端到端边缘视觉应用。我们以目光估计为案例，评估IMX500并将其与其他边缘平台（如Google Coral Dev Micro和Sony Spresense）进行比较。我们提出TinyTracker，一种针对二维目光估计的高效全量化模型，旨在最大化本研究所述边缘视觉系统的性能。与iTracker[1]相比，TinyTracker实现了41倍的尺寸缩减（600 Kb），而目光估计精度无明显损失（全量化时最大误差为0.16厘米）。TinyTracker部署于索尼IMX500视觉传感器上，端到端延迟约为19毫秒。摄像头读取、处理并将像素传输至加速器约需17.9毫秒。网络推断时间为0.86毫秒，另加0.24毫秒用于从传感器检索结果。整个端到端系统的总能耗为4.9毫焦，其中推断能耗为0.06毫焦。端到端研究表明，IMX500比CoralMicro快1.7倍（19毫秒对比34.4毫秒），且能效高出7倍（4.9毫焦对比34.2毫焦）。