EdgePC: Efficient Deep Learning Analytics for Point Clouds on Edge Devices
Ziyu Ying, Sandeepa Bhuyan, Yan Kang, Yingtian Zhang, Mahmut Kandemir, Chita R. Das
Abstract
Recently, point cloud (PC) has gained popularity in modeling various 3D objects (including both synthetic and real-life) and has been extensively utilized in a wide range of applications such as AR/VR, 3D reconstruction, and autonomous driving. For such applications, it is critical to analyze/understand the surrounding scenes properly. To achieve this, deep learning based methods (e.g., convolutional neural networks (CNNs)) have been widely employed for higher accuracy. Unlike the deep learning on conventional 2D images/videos, where the feature computation (matrix multiplication) is the major bottleneck, in point cloud-based CNNs, the sample and neighbor search stages are the primary bottlenecks, and collectively contribute to 54% (up to 80%) of the overall execution latency on a typical edge device. While prior efforts have attempted to solve this issue by designing custom ASICs or pipelining the neighbor search with other stages, to our knowledge, none of them has tried to "structurize" the unstructured PC data for improving computational efficiency.