Random memristor-based dynamic graph CNN for efficient point cloud learning at the edge

Abstract

The broad integration of 3D sensors into devices like smartphones and AR/VR headsets has led to a surge in 3D data, with point clouds becoming a mainstream representation method. Efficient real-time learning of point cloud data on edge devices is crucial for applications such as autonomous vehicles and embodied AI. Traditional machine learning models on digital processors face limitations, with software challenges like high training complexity, and hardware challenges such as large time and energy overheads due to von Neumann bottleneck. To address this, we propose a software-hardware co-designed random memristor-based dynamic graph CNN (RDGCNN). Software-wise, we transform point cloud into graph, and propose random EdgeConv for efficient hierarchical and topological features extraction. Hardware-wise, leveraging memristor’s intrinsic stochasticity and in-memory computing capability, we achieve significant reductions in training complexity and energy consumption. RDGCNN demonstrates high accuracy and efficiency across various point cloud tasks, paving the way for future edge 3D vision.