research-article

Point Cloud Acceleration by Exploiting Geometric Similarity

Authors:

Kenli LiAuthors Info & Claims

MICRO '23: Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture

Pages 1135 - 1147

https://doi.org/10.1145/3613424.3614290

Published: 08 December 2023 Publication History

Abstract

Deep learning on point clouds has attracted increasing attention for various emerging 3D computer vision applications, such as autonomous driving, robotics, and virtual reality. These applications interact with people in real-time on edge devices and thus require low latency and low energy. To accelerate the execution of deep neural networks (DNNs) on point clouds, some customized accelerators have been proposed, which achieved a significantly higher performance with reduced energy consumption than GPUs and existing DNN accelerators.

In this work, we reveal that DNNs execution on geometrically adjacent points exhibits similar values and relations, and exhibits a large amount of redundant computation and communication due to the correlations. To address this issue, we propose GDPCA, a geometry-aware differential point cloud accelerator, which can exploit geometric similarity to reduce these redundancies for point cloud neural networks. GDPCA is supported by an algorithm and architecture co-design. Our proposed algorithm can discover and reduce computation and communication redundancies with geometry-aware and differential execution mechanisms. Then a novel architecture is designed to support the proposed algorithm and transform the redundancy reduction into performance improvement. GDPCA performs the same computations and gives the same accuracy as traditional point cloud neural networks. To the best of our knowledge, GDPCA is the first accelerator that can reduce execution redundancies for point cloud neural networks by exploiting geometric similarity. Our proposed GDPCA system gains an average of 2.9 × speedup and 2.7 × energy efficiency over state-of-the-art accelerators for point cloud neural networks.

References

[1]

Jorge Albericio, Alberto Delmás, Patrick Judd, Sayeh Sharify, Gerard O’Leary, Roman Genov, and Andreas Moshovos. 2017. Bit-pragmatic deep neural network computing. In Proceedings of the 50th Annual IEEE/ACM International Symposium on Microarchitecture. 382–394.

Digital Library

[2]

Waleed Ali, Sherif Abdelkarim, Mahmoud Zidan, Mohamed Zahran, and Ahmad El Sallab. 2018. Yolo3d: End-to-end real-time 3d oriented object bounding box detection from lidar point cloud. In Proceedings of the European conference on computer vision (ECCV) workshops. 0–0.

[3]

Cen Chen, Kenli Li, Yangfan Li, and Xiaofeng Zou. 2022. ReGNN: A redundancy-eliminated graph neural networks accelerator. In 2022 IEEE International Symposium on High-Performance Computer Architecture (HPCA). IEEE, 429–443.

[4]

Cen Chen, Kenli Li, Xiaofeng Zou, and Yangfan Li. 2021. Dygnn: Algorithm and architecture support of dynamic pruning for graph neural networks. In 2021 58th ACM/IEEE Design Automation Conference (DAC). IEEE, 1201–1206.

Digital Library

[5]

Siheng Chen, Baoan Liu, Chen Feng, Carlos Vallespi-Gonzalez, and Carl Wellington. 2020. 3d point cloud processing and learning for autonomous driving: Impacting map creation, localization, and perception. IEEE Signal Processing Magazine 38, 1 (2020), 68–86.

[6]

Yaodong Cui, Ren Chen, Wenbo Chu, Long Chen, Daxin Tian, Ying Li, and Dongpu Cao. 2021. Deep learning for image and point cloud fusion in autonomous driving: A review. IEEE Transactions on Intelligent Transportation Systems (2021).

[7]

Yu Feng, Gunnar Hammonds, Yiming Gan, and Yuhao Zhu. 2022. Crescent: taming memory irregularities for accelerating deep point cloud analytics. In Proceedings of the 49th Annual International Symposium on Computer Architecture. 962–977.

Digital Library

[8]

Yu Feng, Boyuan Tian, Tiancheng Xu, Paul Whatmough, and Yuhao Zhu. 2020. Mesorasi: Architecture support for point cloud analytics via delayed-aggregation. In 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). IEEE, 1037–1050.

[9]

Chang Gao, Tobi Delbruck, and Shih-Chii Liu. 2022. Spartus: A 9.4 TOp/s FPGA-based LSTM accelerator exploiting spatio-temporal sparsity. IEEE Transactions on Neural Networks and Learning Systems (2022).

[10]

Chang Gao, Daniel Neil, Enea Ceolini, Shih-Chii Liu, and Tobi Delbruck. 2018. DeltaRNN: A power-efficient recurrent neural network accelerator. In Proceedings of the 2018 ACM/SIGDA International Symposium on Field-Programmable Gate Arrays. 21–30.

Digital Library

[11]

Chang Gao, Antonio Rios-Navarro, Xi Chen, Shih-Chii Liu, and Tobi Delbruck. 2020. EdgeDRNN: Recurrent neural network accelerator for edge inference. IEEE Journal on Emerging and Selected Topics in Circuits and Systems 10, 4 (2020), 419–432.

[12]

Andreas Geiger, Philip Lenz, and Raquel Urtasun. 2012. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE conference on computer vision and pattern recognition. IEEE, 3354–3361.

[13]

Yulan Guo, Mohammed Bennamoun, Ferdous Sohel, Min Lu, and Jianwei Wan. 2014. 3D object recognition in cluttered scenes with local surface features: A survey. IEEE transactions on pattern analysis and machine intelligence 36, 11 (2014), 2270–2287.

[14]

Yulan Guo, Ferdous Sohel, Mohammed Bennamoun, Min Lu, and Jianwei Wan. 2013. Rotational projection statistics for 3D local surface description and object recognition. International journal of computer vision 105, 1 (2013), 63–86.

[15]

Yulan Guo, Hanyun Wang, Qingyong Hu, Hao Liu, Li Liu, and Mohammed Bennamoun. 2020. Deep learning for 3d point clouds: A survey. IEEE transactions on pattern analysis and machine intelligence (2020).

Digital Library

[16]

Sagar Imambi, Kolla Bhanu Prakash, and GR Kanagachidambaresan. 2021. PyTorch. Programming with TensorFlow: Solution for Edge Computing Applications (2021), 87–104.

[17]

Yoongu Kim, Weikun Yang, and Onur Mutlu. 2015. Ramulator: A fast and extensible DRAM simulator. IEEE Computer architecture letters 15, 1 (2015), 45–49.

[18]

Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom. 2019. Pointpillars: Fast encoders for object detection from point clouds. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 12697–12705.

[19]

Yujun Lin, Zhekai Zhang, Haotian Tang, Hanrui Wang, and Song Han. 2021. Pointacc: Efficient point cloud accelerator. In MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture. 449–461.

Digital Library

[20]

Erik Lindholm, John Nickolls, Stuart Oberman, and John Montrym. 2008. NVIDIA Tesla: A unified graphics and computing architecture. IEEE micro 28, 2 (2008), 39–55.

Digital Library

[21]

Yongcheng Liu, Bin Fan, Gaofeng Meng, Jiwen Lu, Shiming Xiang, and Chunhong Pan. 2019. Densepoint: Learning densely contextual representation for efficient point cloud processing. In Proceedings of the IEEE/CVF international conference on computer vision. 5239–5248.

[22]

Zhijian Liu, Alexander Amini, Sibo Zhu, Sertac Karaman, Song Han, and Daniela Rus. 2021. Efficient and Robust LiDAR-Based End-to-End Navigation. arXiv e-prints (2021), arXiv–2105.

[23]

Zhijian Liu, Xinyu Yang, Haotian Tang, Shang Yang, and Song Han. 2023. FlatFormer: Flattened Window Attention for Efficient Point Cloud Transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 1200–1211.

[24]

Mostafa Mahmoud, Kevin Siu, and Andreas Moshovos. 2018. Diffy: A Déjà vu-free differential deep neural network accelerator. In 2018 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). IEEE, 134–147.

Digital Library

[25]

Anurag Mukkara, Nathan Beckmann, Maleen Abeydeera, Xiaosong Ma, and Daniel Sanchez. 2018. Exploiting locality in graph analytics through hardware-accelerated traversal scheduling. In 2018 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO). IEEE, 1–14.

Digital Library

[26]

Ayrton Oliver, Steven Kang, Burkhard C Wünsche, and Bruce MacDonald. 2012. Using the Kinect as a navigation sensor for mobile robotics. In Proceedings of the 27th conference on image and vision computing New Zealand. 509–514.

Digital Library

[27]

Chunghyun Park, Yoonwoo Jeong, Minsu Cho, and Jaesik Park. 2022. Fast point transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 16949–16958.

[28]

Alessio Pierluigi Placitelli and Luigi Gallo. 2011. Low-cost augmented reality systems via 3D point cloud sensors. In 2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems. IEEE, 188–192.

Digital Library

[29]

François Pomerleau, Francis Colas, Roland Siegwart, 2015. A review of point cloud registration algorithms for mobile robotics. Foundations and Trends® in Robotics 4, 1 (2015), 1–104.

[30]

Charles R Qi, Wei Liu, Chenxia Wu, Hao Su, and Leonidas J Guibas. 2018. Frustum pointnets for 3d object detection from rgb-d data. In Proceedings of the IEEE conference on computer vision and pattern recognition. 918–927.

[31]

Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. 2017. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 652–660.

[32]

Charles Ruizhongtai Qi, Li Yi, Hao Su, and Leonidas J Guibas. 2017. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems 30 (2017).

[33]

Marc Riera, Jose-Maria Arnau, and Antonio González. 2018. Computation reuse in DNNs by exploiting input similarity. In 2018 ACM/IEEE 45th Annual International Symposium on Computer Architecture (ISCA). IEEE, 57–68.

Digital Library

[34]

Weijing Shi and Raj Rajkumar. 2020. Point-gnn: Graph neural network for 3d object detection in a point cloud. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 1711–1719.

[35]

Martin Simon, Karl Amende, Andrea Kraus, Jens Honer, Timo Samann, Hauke Kaulbersch, Stefan Milz, and Horst Michael Gross. 2019. Complexer-yolo: Real-time 3d object detection and tracking on semantic point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 0–0.

[36]

Jonathan Dyssel Stets, Yongbin Sun, Wiley Corning, and Scott W Greenwald. 2017. Visualization and labeling of point clouds in virtual reality. In SIGGRAPH Asia 2017 Posters. 1–2.

[37]

S. Thoziyoor, N. Muralimanohar, and N. P. Jouppi. [n. d.]. CACTI 5.0. Date ([n. d.]).

[38]

Peng-Shuai Wang, Yang Liu, Yu-Xiao Guo, Chun-Yu Sun, and Xin Tong. 2017. O-cnn: Octree-based convolutional neural networks for 3d shape analysis. ACM Transactions On Graphics (TOG) 36, 4 (2017), 1–11.

Digital Library

[39]

Yue Wang, Yongbin Sun, Ziwei Liu, Sanjay E Sarma, Michael M Bronstein, and Justin M Solomon. 2019. Dynamic graph cnn for learning on point clouds. Acm Transactions On Graphics (tog) 38, 5 (2019), 1–12.

Digital Library

[40]

Yue Wang, Shusheng Zhang, Bile Wan, Weiping He, and Xiaoliang Bai. 2018. Point cloud and visual feature-based tracking method for an augmented reality-aided mechanical assembly system. The International Journal of Advanced Manufacturing Technology 99, 9 (2018), 2341–2352.

[41]

Zilun Wang, Wendong Mao, Peixiang Yang, Zhongfeng Wang, and Jun Lin. 2022. An Efficient FPGA Accelerator for Point Cloud. In 2022 IEEE 35th International System-on-Chip Conference (SOCC). IEEE, 1–6.

[42]

Mark Whitty, Stephen Cossell, Kim Son Dang, Jose Guivant, and Jayantha Katupitiya. 2010. Autonomous navigation using a real-time 3d point cloud. In 2010 Australasian Conference on Robotics and Automation. 1–3.

[43]

Zhirong Wu, Shuran Song, Aditya Khosla, Fisher Yu, Linguang Zhang, Xiaoou Tang, and Jianxiong Xiao. 2015. 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1912–1920.

[44]

Bin Yang, Wenjie Luo, and Raquel Urtasun. 2018. Pixor: Real-time 3d object detection from point clouds. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. 7652–7660.

[45]

Zetong Yang, Yanan Sun, Shu Liu, and Jiaya Jia. 2020. 3dssd: Point-based 3d single stage object detector. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 11040–11048.

[46]

Xiangyu Yue, Bichen Wu, Sanjit A Seshia, Kurt Keutzer, and Alberto L Sangiovanni-Vincentelli. 2018. A lidar point cloud generator: from a virtual world to autonomous driving. In Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval. 458–464.

Digital Library

[47]

Jie-Fang Zhang and Zhengya Zhang. 2021. Point-X: A spatial-locality-aware architecture for energy-efficient graph-based point-cloud deep learning. In MICRO-54: 54th Annual IEEE/ACM International Symposium on Microarchitecture. 1078–1090.

Digital Library

[48]

Kuangen Zhang, Ming Hao, Jing Wang, Clarence W de Silva, and Chenglong Fu. 2019. Linked dynamic graph cnn: Learning on point cloud via linking hierarchical features. arXiv preprint arXiv:1904.10014 (2019).

[49]

Yifan Zhang, Qingyong Hu, Guoquan Xu, Yanxin Ma, Jianwei Wan, and Yulan Guo. 2022. Not all points are equal: Learning highly efficient point-based detectors for 3d lidar point clouds. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 18953–18962.

[50]

Yin Zhou and Oncel Tuzel. 2018. Voxelnet: End-to-end learning for point cloud based 3d object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4490–4499.

[51]

Xiaofeng Zou, Kenli Li, and Cen Chen. 2020. Multilevel attention based u-shape graph neural network for point clouds learning. IEEE Transactions on Industrial Informatics 18, 1 (2020), 448–456.

Cited By

Li YLi MChen CZou XShao HTang FLi K(2025) SimDiff : Point Cloud Acceleration by Utilizing Spatial Similarity and Differential Execution IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.344580844:2(568-581)Online publication date: Feb-2025
https://doi.org/10.1109/TCAD.2024.3445808
Chen XChen HYang C(2024)PointCIM: A Computing-in-Memory Architecture for Accelerating Deep Point Cloud Analytics2024 57th IEEE/ACM International Symposium on Microarchitecture (MICRO)10.1109/MICRO61859.2024.00097(1309-1322)Online publication date: 2-Nov-2024
https://doi.org/10.1109/MICRO61859.2024.00097
Feng XSun WTang CLin XYue JYang HLiu Y(2024)A 28-nm Energy-Efficient Sparse Neural Network Processor for Point Cloud Applications Using Block-Wise Online Neighbor SearchingIEEE Journal of Solid-State Circuits10.1109/JSSC.2024.338687859:9(3070-3081)Online publication date: Sep-2024
https://doi.org/10.1109/JSSC.2024.3386878

Index Terms

Point Cloud Acceleration by Exploiting Geometric Similarity
1. Computer systems organization
  1. Architectures
    1. Other architectures
      1. Neural networks
2. Hardware
  1. Integrated circuits
    1. Reconfigurable logic and FPGAs
      1. Hardware accelerators

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cover image ACM Conferences

MICRO '23: Proceedings of the 56th Annual IEEE/ACM International Symposium on Microarchitecture

October 2023

1528 pages

ISBN:9798400703294

DOI:10.1145/3613424

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 08 December 2023

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MICRO '23: 56th Annual IEEE/ACM International Symposium on Microarchitecture

October 28 - November 1, 2023

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Cited By

Li YLi MChen CZou XShao HTang FLi K(2025) SimDiff : Point Cloud Acceleration by Utilizing Spatial Similarity and Differential Execution IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2024.344580844:2(568-581)Online publication date: Feb-2025
https://doi.org/10.1109/TCAD.2024.3445808
Chen XChen HYang C(2024)PointCIM: A Computing-in-Memory Architecture for Accelerating Deep Point Cloud Analytics2024 57th IEEE/ACM International Symposium on Microarchitecture (MICRO)10.1109/MICRO61859.2024.00097(1309-1322)Online publication date: 2-Nov-2024
https://doi.org/10.1109/MICRO61859.2024.00097
Feng XSun WTang CLin XYue JYang HLiu Y(2024)A 28-nm Energy-Efficient Sparse Neural Network Processor for Point Cloud Applications Using Block-Wise Online Neighbor SearchingIEEE Journal of Solid-State Circuits10.1109/JSSC.2024.338687859:9(3070-3081)Online publication date: Sep-2024
https://doi.org/10.1109/JSSC.2024.3386878

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