TAAC: Task Allocation Meets Approximate Computing for Internet of Things

Wanli Yu, Ardalan Najafi, Yarib Nevarez, Yanqiu Huang, Alberto Garcia-Ortiz

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

3 Citations (Scopus)
7 Downloads (Pure)


Ultra-low-power operation, as required by Internet of Things (IoT) systems, requires to address energy consumption at all levels from circuit to system. Two of the promising solutions at circuit and system levels are approximate computing and energy aware task allocation, respectively. However, the existing task allocation approaches are designed without considering the aspect of approximate computing. For the first time, this work proposes an optimal Task Allocation algorithm taking the Approximate Computing into account (TAAC) to fill this gap. The problem of tasks assignment and executing modes selection (approximate or exact modes of the tasks) can be efficiently solved by formulating it as a linear programming problem. The extensive simulation results show that the proposed TAAC algorithm significantly outperforms the previous approaches.
Original languageEnglish
Title of host publication2020 IEEE International Symposium on Circuits and Systems (ISCAS)
Place of PublicationPiscataway, NJ
Number of pages5
ISBN (Electronic)9781728133201
ISBN (Print)978-1-7281-3320-1
Publication statusPublished - 28 Sept 2020
Event52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020 - Virtual Conference, Virtual, Online, Spain
Duration: 10 Oct 202021 Oct 2020
Conference number: 52

Publication series

NameIEEE International Symposium on Circuits and Systems (ISCAS)
ISSN (Print)2158-1525


Conference52nd IEEE International Symposium on Circuits and Systems, ISCAS 2020
Abbreviated titleISCAS 2020
CityVirtual, Online
Internet address


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