With the widespread adoption of 3D sensors in various devices, such as mixed reality headsets and smartphones, there has been a significant increase in the volume of 3D data. Point clouds have emerged as a popular method for representing 3D objects, enabling applications in augmented reality, autonomous driving, and robotics. The need for efficient learning of point cloud data on edge devices has become crucial, highlighting the importance of real-time processing with limited computing resources.
Traditional machine learning models face challenges in handling point cloud data due to high training complexity and energy overheads. To address these challenges, a software-hardware co-designed approach called random memristor-based dynamic graph CNN (RDGCNN) has been proposed. This approach transforms point cloud data into graphs, enabling efficient hierarchical and topological feature extraction. Leveraging memristor technology, significant reductions in training complexity and energy consumption have been achieved.
The RDGCNN model has demonstrated high accuracy and efficiency across various point cloud tasks, including classification, part segmentation, and semantic segmentation. By converting point cloud data into graph representations and utilizing memristor-based hardware for in-memory computing, the RDGCNN model has shown promising results in handling complex 3D datasets. The co-design approach not only reduces energy consumption but also minimizes training overhead, making it suitable for real-time edge applications.
Experimental results on ModelNet40, ShapeNet, and S3DIS datasets have validated the effectiveness of the RDGCNN model for point cloud tasks. The model achieved classification accuracy of 89.75%, mean Intersection over Union (mIoU) of 83.67%, and 46.35% on different datasets, showcasing its potential for efficient point cloud learning at the edge. The hardware-software co-design approach presented in this study opens up new possibilities for handling 3D data efficiently and economically in edge computing applications.
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