In the paper, "Stephan Patrick Baller, Anshul Jindal, Mohak Chadha, and Michael Gerndt" present and compare the performance in terms of inference time and power consumption of the four SoCs: Asus Tinker Edge R, Raspberry Pi 4, Google Coral Dev Board, Nvidia Jetson Nano, and one microcontroller: Arduino Nano 33 BLE, on different deep learning models and frameworks.
They also provide a method for measuring power consumption, inference time and accuracy for the devices, which can be easily extended to other devices. The results showcase that, for Tensorflow based quantized model, the Google Coral Dev Board delivers the best performance, both for inference time and power consumption. For a low fraction of inference computation time, i.e. less than 29.3% of the time for MobileNetV2, the Jetson Nano performs faster than the other devices.