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Improving NVM Lifetime Using Task Stack Migration on Low-End MCU-Based Devicesopen access

Authors
Lee, JeongminJang, MoonseokWang, KexinSong, InyeongJeong, HyeonggyuJeong, JinwooSong, Yong HoChoi, Jungwook
Issue Date
Dec-2022
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Embedded software; memory management; nonvolatile memory; real-time systems
Citation
IEEE ACCESS, v.10, pp.125319 - 125333
Indexed
SCIE
SCOPUS
Journal Title
IEEE ACCESS
Volume
10
Start Page
125319
End Page
125333
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/182140
DOI
10.1109/ACCESS.2022.3225193
ISSN
2169-3536
Abstract
Tiny embedded devices are cost and energy-sensitive, and high-density emerging non-volatile memory (NVM) can help reduce energy consumption at a fraction of the cost. However, high-density NVM has low write endurance compared to volatile memory, so it is vulnerable to write concentration. Most NVM lifetime improvement studies in the existing embedded environment have distributed writes by modifying the mapping relationship between physical and logical addresses. However, applying the existing wear leveling techniques in low-end MCUs such as ARM Cortex M3/M4 that use only physical addresses is hard. Therefore, we wear-level the write-heavy stack area to improve the NVM lifetime in low-end MCUs. However, since the stack of bare metal applications is difficult to move during runtime, we implement the migration function targeting the task stack of FreeRTOS. The task stack moves based on time, and to avoid the pointer validation problem caused by the movement of the task stack, we migrate the stack under safe conditions. In addition, FreeRTOS uses a single heap to preferentially allocate to low free space, which reduces the degree of freedom where the stack moves, reducing the effect of distributing the writes. To alleviate this problem, we add another heap for the stack migration and introduce circular dynamic allocation in the heap. Through our experiments, the proposed method was about 19.6% larger than the ideal case of maximum write, and the computational overhead was about 0.2%.
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