An FPGA based Parallel Implementation for Point Cloud Neural Network Point cloud is an important type of data structure for 3d visual information processing. Convolutional Neural Networks(CNNs) like Pointnet 4 are proposed for effective feature learning of point clouds recently. Pointnet is a novel type of neural network that directly consumes point clouds whcih can select informative points. In this paper, we design a fast, lowpower FPGA accelerator for Pointnet. Most of the current FPGA accelerators currently do not handle point cloud data very well. The network designed for the FPGA implement, called OPointnet, optimizes Pointnetu0027s nonlinear implementation, multilayer sensing layer and maximum pooling layer by analyzing the implementation of the network and the spacetime complexity. Experiments show that the computational performance is 1.208 GMACs with 2.149W power dissipation, and the the accuracy rate of the OPointnet is 88.48%, which outperforms previous approaches.

FPNet: Customized Convolutional Neural Network for FPGA Platforms The Convolutional Neural Network (CNN) has been widely adopted in various applications, which include object detection mobile robot vision, image search engine, etc. And due to the computingintensive and memoryintensive features of CNN models, specialized hardware accelerators, like ApplicationSpecificIntegratedCircuit (ASIC) and Field Programmable Gate Arrays (FPGA) have been widely utilized in edge devices. Among all the neural network specialized hardware accelerators, an FPGA accelerator stands out for its flexibility, short timetomarket, and energy efficiency. Previous works about FPGA accelerator designs are mostly changing hardware configuration to fit the CNN model structure. In this paper, we propose an automated neural network architecture search approach utilizing reinforcement learning to design customized convolutional neural network model for specialized FPGA platforms.