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

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.

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Publication Details

Journal
npj Unconventional Computing
Published
2024-08-21
DOI
https://doi.org/10.1038/s44335-024-00006-0
Citations
1
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.21

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

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npj Unconventional Computing
Advanced Memory and Neural Computing
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article

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

Dashan Shang, Meng Xu, Songqi Wang, Hegan Chen, Shaocong Wang, Xiaoshan Wu, Dingchen Wang, Woyu Zhang, Zhongrui Wang, Xi Chen, Ning Lin, Jichang Yang, Xiaojuan Qi, Bo Wang, Yue Zhang, Yifei Yu
article en
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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.

npj Unconventional ComputingVol. 1(1)
Chinese Academy of Sciences (CN), Hong Kong Science and Technology Parks Corporation (HK), Southern University of Science and Technology (CN), Institute of Microelectronics (CN), University of Chinese Academy of Sciences (CN), University of Hong Kong (HK)
National Natural Science Foundation of China, National Key Research and Development Program of China
Affordable and clean energy
Openalex Percentile: Top 49%
Advanced Memory and Neural Computing
0.21
DOIOpen Access PDFOpenAlex
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