  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)
