TY - JOUR
T1 - Adaptive approximate computing in edge AI and IoT applications
T2 - A review
AU - Damsgaard, Hans Jakob
AU - Grenier, Antoine
AU - Katare, Dewant
AU - Taufique, Zain
AU - Shakibhamedan, Salar
AU - Troccoli, Tiago
AU - Chatzitsompanis, Georgios
AU - Kanduri, Anil
AU - Ding, Aaron Yi
AU - More Authors, null
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Approximate computing
KW - Autonomous driving
KW - Edge computing
KW - Positioning
KW - Smart sensing
UR - http://www.scopus.com/inward/record.url?scp=85188557932&partnerID=8YFLogxK
U2 - 10.1016/j.sysarc.2024.103114
DO - 10.1016/j.sysarc.2024.103114
M3 - Review article
AN - SCOPUS:85188557932
SN - 1383-7621
VL - 150
JO - Journal of Systems Architecture
JF - Journal of Systems Architecture
M1 - 103114
ER -