Q-learning Based Backup for Energy Harvesting Powered Embedded Systems

被引：0

作者：

Fan, Wei ^{[1
]}

Zhang, Yujie ^{[1
]}

Song, Weining ^{[1
]}

Zhao, Mengying ^{[1
]}

Shen, Zhaoyan ^{[1
]}

Jia, Zhiping ^{[1
]}

机构：

[1] Shandong Univ, Sch Comp Sci & Technol, Qingdao, Peoples R China

来源：

PROCEEDINGS OF THE 2020 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE 2020) | 2020年

基金：

美国国家科学基金会;

关键词：

Energy harvesting system; Non-volatile processor; Q-learning; Forward progress; NONVOLATILE SRAM;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Non-volatile processors (NVPs) are used in energy harvesting powered embedded systems to preserve data across interruptions. In NVP systems, volatile data are backed up to non-volatile memory upon power failures and resumed after power comes back. Traditionally, backup is triggered immediately when energy warning occurs. However, it is also possible to more aggressively utilize the residual energy for program execution to improve forward progress. In this work, we propose a Q-learning based backup strategy to achieve maximal forward progress in energy harvesting powered intermittent embedded systems. The experimental results show an average of 307.4% and 43.4% improved forward progress compared with traditional instant backup and the most related work, respectively.

引用

页码：1247 / 1252

页数：6

共 18 条

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