Bulletin of Chinese Academy of Sciences (Chinese Version)
Keywords
autonomous machine computing; artificial intelligence; advanced semiconductors
Document Type
Policy & Management Research
Abstract
Autonomous machine computing, an innovative blend of algorithms, software, and cutting-edge computing hardware, is poised to be the next major paradigm shift in the global economy, following personal, mobile, and cloud computing. This study delves into the research and commercialization of the robotics industry, underscoring the critical importance of establishing a comprehensive autonomous machine computing ecosystem. This study argues that autonomous machine computing necessitates a complete ecosystem that encompasses applications, programming languages, and the foundational hardware architectures, and presents a comprehensive review of significant research contributions across these areas. Moreover, the study explores the synergy between autonomous machine computing and advancements in semiconductor technology, highlighting how such technological strides will significantly broaden the capabilities of robots, particularly in terms of sensing, computing, and communication. Looking ahead, the authors predict that within the next decade, autonomous machine computing will deeply integrate into all societal sectors, profoundly influencing the global economy. From enhancing smart homes to revolutionizing healthcare, agriculture, and beyond, robotics computing is set to be the linchpin of innovation, opening up unprecedented business opportunities while substantially elevating the quality of life. Consequently, promptly defining and advancing the architecture of autonomous machine computing is crucial for positioning our nation at the forefront of the global robotics industry’s major breakthroughs and developments over the coming decade.
First page
1956
Last Page
1965
Language
Chinese
Publisher
Bulletin of Chinese Academy of Sciences
References
1 Liu S S, Kong A, Huang Y Z, et al. Autonomous mobile clinics. Bulletin of the World Health Organization, 2022, 100(9): 527-527A.
2 Liu S S, Liu L K, Tang J, et al. Edge computing for autonomous driving: Opportunities and challenges. Proceedings of the IEEE, 2019, 107(8): 1697-1716.
3 Ragan-Kelley J, Barnes C, Adams A, et al. Halide: A language and compiler for optimizing parallelism, locality, and recomputation in image processing pipelines. ACM SIGPLAN Notices, 48(6): 519-530.
4 Liu S S, Tang J, Zhang Z, et al. Computer architectures for autonomous driving. Computer, 2017, 50(8):18-25.
5 Gan Y M, Bo, Y, Tian, B Y, et al. Eudoxus: Characterizing and accelerating localization in autonomous machines industry track paper// 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA). Seoul: IEEE, 2021: 827-840.
6 Hao Y H, Gan Y M, Yu B, et al. BLITZCRANK: Factor graph accelerator for motion planning// 2023 60th ACM/IEEE Design Automation Conference (DAC). San Francisco: IEEE, 2023.
7 Yu B, Tang J, Liu S S. Autonomous driving digital twin empowered design automation: An industry perspective// 2023 60th ACM/IEEE Design Automation Conference (DAC). San Francisco: IEEE, 2023.
8 Suleiman A, Zhang Z D, Carlone L, et al. Navion: A 2-mW fully integrated real-time visual-inertial odometry accelerator for autonomous navigation of nano drones. IEEE Journal of Solid-State Circuits, 2019, 54(4): 1106-1119.
9 Liu Q, Wan Z S, Yu B, et al. An energy-efficient and runtime-reconfigurable FPGA-based accelerator for robotic localization systems// 2022 IEEE Custom Integrated Circuits Conference (CICC). Newport Beach: IEEE, 2022.
10 Fu D C, Li X, Wen L C, et al. Drive like a human: Rethinking autonomous driving with large language models// 2024 IEEE/CVF Winter Conference on Applications of Computer Vision. Waikoloa: IEEE, 2024: 910-919.
11 Tang Y, Da Costa A A B, Zhang J, et al. Domain knowledge distillation from large language model: An empirical study in the autonomous driving domain// 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC). Bilbao: IEEE, 2023.
12 Chen Y T, Nazhamaiti M, Xu H, et al. All-analog photoelectronic chip for high-speed vision tasks. Nature, 2023, 623: 48-57.
13 Chen G B, Wiede C, Kokozinski R. Data processing approaches on SPAD-based d-TOF LiDAR systems: A review. IEEE Sensors Journal, 2021, 21(5): 5656-5667.
14 Li Z C, Pan J L, Hu H W, et al. Recent advances in new materials for 6G communications. Advanced Electronic Materials, 2022, 8(3): 2100978.
15 Brohan A, Brown N, Carbajal J, et al. Rt-2: Vision-language-action models transfer web knowledge to robotic control. arXiv, doi: arXiv:2307.15818v1.
16 李国杰. 智能化科研(AI4R):第五科研范式. 中国科学院院刊, 2024, 39(1): 1-9.Li G J. AI4R: The fifth research paradigm. Bulletin of Chinese Academy of Sciences, 2024, 39(1): 1-9. (in Chinese)
Recommended Citation
LIU, Shaoshan; GAN, Yiming; and HAN, Yinhe
(2024)
"Overview on autonomous machine computing,"
Bulletin of Chinese Academy of Sciences (Chinese Version): Vol. 39
:
Iss.
11
, Article 14.
DOI: https://doi.org/10.16418/j.issn.1000-3045.20240127002
Available at:
https://bulletinofcas.researchcommons.org/journal/vol39/iss11/14