Abstract
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)
Abstract (translated)
智能边缘视觉任务的关键挑战之一是确保由于它们对边缘平台通常繁重计算负载，导致功耗和延迟效率低下。本文利用索尼IMX500第一个"AI在传感器"视觉平台，实现了超快速和超低功耗的端到端边缘视觉应用。我们通过探索目光估计作为一个案例研究来评估IMX500和比较它与其他边缘平台，如谷歌CoralDev Micro和索尼Spresense。我们提出了TinyTracker，一个高效且量化模型，专为最大化考虑本研究中使用的边缘视觉系统的性能而设计。TinyTracker在未显著降低目光估计精度（完全量化时最大为0.16厘米）的情况下，实现了41倍于iTracker [1]的尺寸缩小（600Kb）。TinyTracker在索尼IMX500视觉传感器上的部署导致端到端延迟约为19毫秒。相机读取、处理和传输像素到加速器的时间大约为17.9毫秒。网络的推理时间为0.86毫秒，此外还有0.24毫秒用于从传感器中检索结果。端到端系统的总功耗为4.9毫焦，包括0.06毫焦的推理功耗。端到端研究表明，IMX500是CoralMicro的1.7倍（19毫秒 vs 34.4毫秒）和7倍更高效的能源效率（4.9毫焦 vs 34.2毫焦）。
URL
https://arxiv.org/abs/2307.07813
