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Point cloud is an important type of geometric data structure for many embedded applications such as autonomous driving and augmented reality. Current Point Cloud Networks (PCNs) have proven to achieve great success in using inference to perform point cloud analysis, including object part segmentation, shape classification, and so on. However, point cloud applications on the computing edge require more than just the inference step. They require an end-to-end (E2E) processing of the point cloud workloads: pre-processing of raw data, input preparation, and inference to perform point cloud analysis. Current PCN approaches to support end-to-end processing of point cloud workload cannot meet the real-time latency requirement on the edge, i.e., the ability of the AI service to keep up with the speed of raw data generation by 3D sensors. Latency for end-to-end processing of the point cloud workloads stems from two reasons: memory-intensive down-sampling in the pre-processing phase and the data structuring step for input preparation in the inference phase. In this paper, we present HgPCN, an end-to-end heterogeneous architecture for real-time embedded point cloud applications. In HgPCN, we introduce two novel methodologies based on spatial indexing to address the two identified bottlenecks. In the Pre-processing Engine of HgPCN, an Octree-Indexed-Sampling method is used to optimize the memory-intensive down-sampling bottleneck of the pre-processing phase. In the Inference Engine, HgPCN extends a commercial DLA with a customized Data Structuring Unit which is based on a Voxel-Expanded Gathering method to fundamentally reduce the workload of the data structuring step in the inference phase. The initial prototype of HgPCN has been implemented on an Intel PAC (Xeon+FPGA) platform. Four commonly available point cloud datasets were used for comparison, running on three baseline devices: Intel Xeon W-2255, Nvidia Xavier NX Jetson GPU, and Nvidia 4060ti GPU. These point cloud datasets were also run on two existing PCN accelerators for comparison: PointACC and Mesorasi. Our results show that for the inference phase, depending on the dataset size, HgPCN achieves speedup from 1.3× to 10.2× vs. PointACC, 2.2× to 16.5× vs. Mesorasi, and 6.4× to 21× vs. Jetson NX GPU. Along with optimization of the memory-intensive down-sampling bottleneck in pre-processing phase, the overall latency shows that HgPCN can reach the real-time requirement by providing end-to-end service with keeping up with the raw data generation rate. 

Genetic programming (GP) is a general, broadly effective procedure by which computable solutions are constructed from high-level objectives. As with other machine-learning endeavors, one continual trend for GP is to exploit ever-larger amounts of parallelism. In this paper, we explore the possibility of accelerating GP by way of modern field-programmable gate arrays (FPGAs), which is motivated by the fact that FPGAs can sometimes leverage larger amounts of both function and data parallelism—common characteristics of GP— when compared to CPUs and GPUs. As a first step towards more general acceleration, we present a preliminary accelerator for the evaluation phase of "tree-based GP"—the original, and still popular, flavor of GP—for which the FPGA dynamically compiles programs of varying shapes and sizes onto a reconfigurable function tree pipeline. Overall, when compared to a recent open-source GPU solution implemented on a modern 8nm process node, our accelerator implemented on an older 20nm FPGA achieves an average speedup of 9.7×. Although our accelerator is 7.9× slower than most examples of a state-of-the-art CPU solution implemented on a recent 7nm process node, we describe future extensions that can make FPGA acceleration provide attractive Pareto-optimal tradeoffs.