ProAI: An Efficient Embedded AI Hardware for Automotive Applications -- a Benchmark Study
This work addresses the problem of power-intensive deep neural network execution in real-time for automotive systems like ADAS and autonomous driving, though it is incremental as it focuses on benchmarking an existing hardware solution.
The paper tackled the need for efficient embedded AI hardware for automotive applications by benchmarking the ProAI system against other single-board computers, showing it achieves nearly twice the FPS per watt compared to a modern laptop and four times that of the Jetson Nano.
Development in the field of Single Board Computers (SBC) have been increasing for several years. They provide a good balance between computing performance and power consumption which is usually required for mobile platforms, like application in vehicles for Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD). However, there is an ever-increasing need of more powerful and efficient SBCs which can run power intensive Deep Neural Networks (DNNs) in real-time and can also satisfy necessary functional safety requirements such as Automotive Safety Integrity Level (ASIL). ProAI is being developed by ZF mainly to run powerful and efficient applications such as multitask DNNs and on top of that it also has the required safety certification for AD. In this work, we compare and discuss state of the art SBC on the basis of power intensive multitask DNN architecture called Multitask-CenterNet with respect to performance measures such as, FPS and power efficiency. As an automotive supercomputer, ProAI delivers an excellent combination of performance and efficiency, managing nearly twice the number of FPS per watt than a modern workstation laptop and almost four times compared to the Jetson Nano. Furthermore, it was also shown that there is still power in reserve for further and more complex tasks on the ProAI, based on the CPU and GPU utilization during the benchmark.