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.
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
