A2 Refereed review article in a scientific journal
Adaptive approximate computing in edge AI and IoT applications: A review
Authors: Damsgaard Hans Jakob, Grenier Antoine, Katare Dewant, Taufique Zain, Shakibhamedan Salar, Troccoli Tiago, Chatzitsompanis Georgios, Kanduri Anil, Ometov Aleksandr, Ding Aaron Yi, Taherinejad Nima, Karakonstantis Georgios, Woods Roger, Nurmi Jari
Publisher: Elsevier
Publication year: 2024
Journal: Journal of Systems Architecture
Journal name in source: Journal of Systems Architecture
Article number: 103114
Volume: 150
ISSN: 1383-7621
eISSN: 1873-6165
DOI: https://doi.org/10.1016/j.sysarc.2024.103114(external)
Web address : https://doi.org/10.1016/j.sysarc.2024.103114(external)
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/387403118(external)
Recent advancements in hardware and software systems have been driven by the deployment of emerging smart health and mobility applications. These developments have modernized the traditional approaches by replacing conventional computing systems with cyber–physical and intelligent systems combining the Internet of Things (IoT) with Edge Artificial Intelligence. Despite the many advantages and opportunities of these systems within various application domains, the scarcity of energy, extensive computing needs, and limited communication must be considered when orchestrating their deployment. Inducing savings in these directions is central to the Approximate Computing (AxC) paradigm, in which the accuracy of some operations is traded off with energy, latency, and/or communication reductions. Unfortunately, the dynamics of the environments in which AxC-equipped IoT systems operate have been paid little attention. We bridge this gap by surveying adaptive AxC techniques applied to three emerging application domains, namely autonomous driving, smart sensing and wearables, and positioning, paying special attention to hardware acceleration. We discuss the challenges of such applications, how adaptive AxC can aid their deployment, and which savings it can bring based on traits of the data and devices involved. Insights arising thereof may serve as inspiration to researchers, engineers, and students active within the considered domains.
Downloadable publication This is an electronic reprint of the original article. |  |
