TY - JOUR
T1 - LATICS: A Low-Overhead Adaptive Task-Based Intermittent Computing System
AU - Liu, Songran
AU - Zhang, Wei
AU - Lv, Mingsong
AU - Chen, Qiulin
AU - Guan, Nan
N1 - Funding Information:
Manuscript received April 17, 2020; revised June 17, 2020; accepted July 6, 2020. Date of publication October 2, 2020; date of current version October 27, 2020. This work was supported in part by the Huawei Innovation Research Program “Collaborative Research Project on Computational Sensing,” and in part by NSFC Project under Grant 61772123. This article was presented in the International Conference on Embedded Software 2020 and appears as part of the ESWEEK-TCAD special issue. (Songran Liu and Wei Zhang contributed equally to this work.) (Corresponding author: Mingsong Lv.) Songran Liu and Mingsong Lv are with the School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China, and also with the Department of Computing, Hong Kong Polytechnic University, Hong Kong (e-mail: [email protected]).
Publisher Copyright:
© 1982-2012 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - Energy harvesting promises to power billions of Internet-of-Things devices without being restricted by battery life. The energy output of harvesters is typically tiny and highly unstable, so the computing system must store program states into nonvolatile memory frequently to preserve the execution progress in the presence of frequent power failures. Task-based intermittent computing is a promising paradigm to provide such capability, where each task executes atomically and only states across task boundaries need to be saved. This article presents LATICS, a low-overhead adaptive task-based intermittent computing system, which dynamically decides the granularity of atomic execution to avoid unnecessarily frequent state saving when energy supply is sufficient. The novel feature of LATICS is to drastically reduce the amount of states to be saved at task boundaries compared with existing solutions. Notably, we disclose that skipping state saving at some task boundary may cause the system to store more states at other places, and thus leads to higher overall overhead. Therefore, LATICS enforces mandatory state saving at certain task boundaries regardless of the current energy condition to reduce state saving overhead. We implement LATICS on a real energy-harvesting platform based on MSP430 and experimentally compare against the state-of-the-art under different settings. The experimental results show that LATICS significantly reduces state saving overhead and improves execution efficiency compared to existing solutions.
AB - Energy harvesting promises to power billions of Internet-of-Things devices without being restricted by battery life. The energy output of harvesters is typically tiny and highly unstable, so the computing system must store program states into nonvolatile memory frequently to preserve the execution progress in the presence of frequent power failures. Task-based intermittent computing is a promising paradigm to provide such capability, where each task executes atomically and only states across task boundaries need to be saved. This article presents LATICS, a low-overhead adaptive task-based intermittent computing system, which dynamically decides the granularity of atomic execution to avoid unnecessarily frequent state saving when energy supply is sufficient. The novel feature of LATICS is to drastically reduce the amount of states to be saved at task boundaries compared with existing solutions. Notably, we disclose that skipping state saving at some task boundary may cause the system to store more states at other places, and thus leads to higher overall overhead. Therefore, LATICS enforces mandatory state saving at certain task boundaries regardless of the current energy condition to reduce state saving overhead. We implement LATICS on a real energy-harvesting platform based on MSP430 and experimentally compare against the state-of-the-art under different settings. The experimental results show that LATICS significantly reduces state saving overhead and improves execution efficiency compared to existing solutions.
KW - Energy harvesting
KW - intermittent computing
KW - task based
KW - task coalescing
UR - http://www.scopus.com/inward/record.url?scp=85096038630&partnerID=8YFLogxK
U2 - 10.1109/TCAD.2020.3012214
DO - 10.1109/TCAD.2020.3012214
M3 - Journal article
AN - SCOPUS:85096038630
SN - 0278-0070
VL - 39
SP - 3711
EP - 3723
JO - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
JF - IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
IS - 11
M1 - 9211403
ER -