Adaptive approximate computing in edge AI and IoT applications: A review

Journal of Systems Architecture(2024)

引用 0|浏览4
暂无评分
摘要
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.
更多
查看译文
关键词
Approximate computing,Autonomous driving,Edge computing,Positioning,Smart sensing
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要