This machine learning study investigates a lowcost edge device integrated with an embedded system having computer vision and resulting in an improved performance in inferencing time and precision of object detection and classification. A primary aim of this study focused on reducing inferencing time and low-power consumption and to enable an embedded device of a competition-ready autonomous humanoid robot and to support real-time object recognition, scene understanding, visual navigation, motion planning, and autonomous navigation of the robot. This study compares processors for inferencing time performance between a central processing unit (CPU), a graphical processing unit (GPU), and a tensor processing unit (TPU). CPUs, GPUs, and TPUs are all processors that can be used for machine learning tasks. Related to the aim of supporting an autonomous humanoid robot, there was an additional effort to observe whether or not there was a significant difference in using a camera having monocular vision versus stereo vision capability. TPU inference time results for this study reflect a 25% reduction in time over the GPU, and a whopping 87.5% reduction in inference time compared to the CPU. Much information in this paper is contributed to the final selection of Google's Coral brand, Edge TPU device. The Arduino Nano 33 BLE Sense Tiny ML Kit was also considered for comparison but due to initial incompatibilities and in the interest of time to complete this study, a decision was made to review the kit in a future experiment.

翻译：本研究探讨了一种集成嵌入式系统的低成本边缘设备，该设备具备计算机视觉功能，并在目标检测与分类的推理时间和精度方面实现了性能提升。本研究的主要目标在于减少推理时间、降低功耗，以构建适用于竞赛级自主仿人机器人的嵌入式设备，并支持其实时目标识别、场景理解、视觉导航、运动规划及自主导航功能。研究比较了中央处理器（CPU）、图形处理器（GPU）与张量处理器（TPU）在推理时间性能上的表现。CPU、GPU和TPU均为可用于机器学习任务的处理器。为支持自主仿人机器人系统，本研究还额外探究了使用单目视觉相机与立体视觉相机是否会产生显著性能差异。实验结果显示，TPU的推理时间较GPU减少25%，较CPU大幅降低87.5%。本文的诸多研究数据为最终选用Google Coral品牌的Edge TPU设备提供了依据。研究过程中亦考虑了Arduino Nano 33 BLE Sense Tiny ML套件进行对比，但因初期兼容性问题及研究时限，决定将其留待后续实验进行评估。