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Point-X: A Spatial-Locality-Aware Architecture for Energy-Efficient Graph-Based Point-Cloud Deep Learning

Authors:

Jie-Fang Zhang,

Zhengya ZhangAuthors Info & Claims

MICRO '21: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture

Pages 1078 - 1090

https://doi.org/10.1145/3466752.3480081

Published: 17 October 2021 Publication History

Abstract

Deep learning on point clouds has attracted increasing attention in the fields of 3D computer vision and robotics. In particular, graph-based point-cloud deep neural networks (DNNs) have demonstrated promising performance in 3D object classification and scene segmentation tasks. However, the scattered and irregular graph-structured data in a graph-based point-cloud DNN cannot be computed efficiently by existing SIMD architectures and accelerators. We present Point-X, an energy-efficient accelerator architecture that extracts and exploits the spatial locality in point cloud data for efficient processing. Point-X uses a clustering method to extract fine-grained and coarse-grained spatial locality from the input point cloud. The clustering maps the point cloud into distributed compute tiles to maximize intra-tile computational parallelism and minimize inter-tile data movement. Point-X employs a chain network-on-chip (NoC) to further reduce the NoC traffic and achieve up to 3.2 × speedup over a traditional mesh NoC. Point-X’s multi-mode dataflow can support all common operations in a graph-based point-cloud DNN, i.e., edge convolution, shared multi-layer perceptron, and fully-connected layers. Point-X is synthesized in a 28nm technology and it demonstrates a throughput of 1307.1 inference/s and an energy efficiency of 604.5 inference/J on the DGCNN workload. Compared to the Nvidia GTX-1080Ti GPU, Point-X shows 4.5 × and 342.9 × improvement in throughput and efficiency, respectively.

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MICRO '21: MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture

October 2021

1322 pages

ISBN:9781450385572

DOI:10.1145/3466752

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Published: 17 October 2021

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MICRO '21: 54th Annual IEEE/ACM International Symposium on Microarchitecture

October 18 - 22, 2021

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https://doi.org/10.1016/j.vlsi.2024.102313
Yoon HKim JDe V(2024)Fused Sampling and Grouping with Search Space Reduction for Efficient Point Cloud AccelerationProceedings of the 61st ACM/IEEE Design Automation Conference10.1145/3649329.3655940(1-6)Online publication date: 23-Jun-2024
https://dl.acm.org/doi/10.1145/3649329.3655940
Lyu DLi ZChen YWang GHe WXu NHe G(2024)FLNA: Flexibly Accelerating Feature Learning Networks for Large-Scale Point Clouds With Efficient Dataflow DecouplingIEEE Transactions on Very Large Scale Integration (VLSI) Systems10.1109/TVLSI.2024.335512632:4(739-751)Online publication date: 30-Jan-2024
https://dl.acm.org/doi/10.1109/TVLSI.2024.3355126
Chen CWu YDai QZhou HXu MYang SHan XYu Y(2024)A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented PerspectiveIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2024.344546346:12(10297-10318)Online publication date: Dec-2024
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Neu MKarle CSchmidt PHöfer JHarbaum TBecker J(2024)A Dynamically Pipelined Dataflow Architecture for Graph Convolutions in Real-Time Event Interpretation2024 IEEE 37th International System-on-Chip Conference (SOCC)10.1109/SOCC62300.2024.10737798(1-6)Online publication date: 16-Sep-2024
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Sia CLim KPhang J(2023)Comparative Study of Point Cloud Classification Using Deep Learning Neural Networks2023 International Conference on Digital Applications, Transformation & Economy (ICDATE)10.1109/ICDATE58146.2023.10248818(1-5)Online publication date: 14-Jul-2023
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