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A synergistic reinforcement learning-based framework design in driving automation

文献类型：期刊论文

作者Qi, Yuqiong2,3; Hu, Yang1; Wu, Haibin2; Li, Shen4; Ye, Xiaochun2; Fan, Dongrui2
刊名COMPUTERS & ELECTRICAL ENGINEERING
出版日期2022-07-01
卷号101页码:15
关键词Autonomous Driving Heterogeneous Multicore AI Accelerator Criteria Reinforcement Learning Scheduling
ISSN号0045-7906
DOI10.1016/j.compeleceng.2022.107989
英文摘要Autonomous driving, which integrates artificial intelligence and the Internet of Things, has piqued the interest of both academics and industry because of its economic and societal benefits. Rigorous accuracy and latency requirements are important for autonomous driving safety. In order to achieve high computation performance in driving automation system, we propose in this paper a heterogeneous multicore AI accelerator (HMAI). At the same time, on the HMAI, how to allocate a large number of real-time tasks to different accelerators remains a notable problem that is worth considering. Theoretically, this problem is NP-complete, and always solved using heuristic-based and guided random-search-based algorithms. However, the global state of HMAI cannot be considered comprehensively in these algorithms, which usually leads to suboptimal allocations. In this paper, we propose FlexAI, a predictive and global scheduling mechanism on HMAI. Specifically, the proposed scheduling algorithm that is based upon deep reinforcement learning (RL). In order to evaluate the quality of strategies produced by RL agent and update the observation of the scheduling agent, two scheduling metrics are proposed: Global State Value (Gvalue), Matching Score (MS) which pays attention to the requirements of various tasks in driving automation system like emergency level. In the experimental, FlexAI achieves up to 80% execution time reduction and 99% resource utilization improvement compared with Min-min, ATA in heuristics, and genetic algorithms, simulated annealing in guided random-search-based algorithms, and unscheduled case.
资助项目National Natural Science Foundation of China[61732018] ; National Natural Science Foundation of China[61872335] ; National Natural Science Foundation of China[61802367] ; Strategic Priority Research Program of Chinese Academy of Sciences[XDC05000000] ; International Partnership Program of Chinese Academy of Sciences[171111KYSB20200002]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000798074000002
出版者PERGAMON-ELSEVIER SCIENCE LTD
源URL[http://119.78.100.204/handle/2XEOYT63/19584]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Qi, Yuqiong
作者单位1.Univ Texas Dallas, Elect Engn Dept, Dallas, TX USA
2.Chinese Acad Sci, Inst Comp Technol, SKLCA, Beijing, Peoples R China
3.Univ Chinese Acad Sci, Beijing, Peoples R China
4.Natl Univ Singapore, Singapore, Singapore
推荐引用方式
GB/T 7714		 Qi, Yuqiong,Hu, Yang,Wu, Haibin,et al. A synergistic reinforcement learning-based framework design in driving automation[J]. COMPUTERS & ELECTRICAL ENGINEERING,2022,101:15.
APA Qi, Yuqiong,Hu, Yang,Wu, Haibin,Li, Shen,Ye, Xiaochun,&Fan, Dongrui.(2022).A synergistic reinforcement learning-based framework design in driving automation.COMPUTERS & ELECTRICAL ENGINEERING,101,15.
MLA Qi, Yuqiong,et al."A synergistic reinforcement learning-based framework design in driving automation".COMPUTERS & ELECTRICAL ENGINEERING 101(2022):15.

入库方式： OAI收割

来源：计算技术研究所

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