作者
Jinkai Zheng, Phil K Mu, Ziqian Man, Tom H Luan, Lin X Cai, Hangguan Shan
发表日期
2021/12/6
研讨会论文
2021 IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)
页码范围
190-196
出版商
IEEE
简介
Autonomous driving is a complex function consisting of multiple parallel AI tasks running at the same time for information sensing, fusion, and decision making. To process the complex computing tasks, an autonomous vehicle is typically equipped with different processing units at the same time, such as CPU, GPU, FPGA, of the different computing capabilities. As the AI tasks have different requirements on the computing resources, a fundamental issue is how to optimally allocate the real-time computation tasks to different processing units (as known as device placement) on board towards maximum utility for autonomous driving. Towards the issue, this paper develops a reinforcement learning algorithm which is based on the proximal policy optimization (PPO) specifically for finding the optimal device placement for running a neural network model. A sequence-to-sequence model is proposed to allocate the …
学术搜索中的文章
J Zheng, PK Mu, Z Man, TH Luan, LX Cai, H Shan - 2021 IEEE International Conferences on Internet of …, 2021