Comparing Computing Platforms for Deep Learning on a Humanoid Robot
This work addresses platform selection for deep learning in robotics, but it is incremental as it benchmarks existing hardware without introducing new methods.
The study compared the Intel NUC7i7BNH and NVIDIA Jetson TX2 computing platforms for running deep neural networks on a humanoid robot, focusing on tasks like pedestrian detection, and found that each platform has trade-offs in computational performance and power consumption.
The goal of this study is to test two different computing platforms with respect to their suitability for running deep networks as part of a humanoid robot software system. One of the platforms is the CPU-centered Intel NUC7i7BNH and the other is a NVIDIA Jetson TX2 system that puts more emphasis on GPU processing. The experiments addressed a number of benchmarking tasks including pedestrian detection using deep neural networks. Some of the results were unexpected but demonstrate that platforms exhibit both advantages and disadvantages when taking computational performance and electrical power requirements of such a system into account.