Intelligent edge vision tasks face the challenge of power and latency efficiency as the computation load is normally heavy for 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. and 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功耗与延迟效率的挑战，因为计算负载通常对边缘平台而言较为繁重。本研究利用索尼IMX500这一首批“传感器内AI”视觉平台，实现超快速且超低功耗的端到端边缘视觉应用。我们以视线估计为案例，评估了IMX500并将其与其他边缘平台（如Google Coral Dev Micro和索尼Spresense）进行对比。我们提出了TinyTracker——一种为最大化本研究中所考虑的边缘视觉系统性能而设计的高效全量化2D视线估计模型。与iTracker[1]相比，TinyTracker实现了41倍的尺寸缩减（600Kb），而视线估计精度无明显损失（全量化情况下最大误差为0.16厘米）。TinyTracker在索尼IMX500视觉传感器上的部署实现了约19毫秒的端到端延迟：相机读取、处理并将像素传输至加速器约需17.9毫秒；网络推理时间为0.86毫秒，额外需0.24毫秒从传感器获取结果。端到端系统总能耗为4.9毫焦，其中推理能耗为0.06毫焦。端到端研究表明，IMX500比CoralMicro快1.7倍（19毫秒 vs 34.4毫秒），能效提升7倍（4.9毫焦 vs 34.2毫焦）。