Hardware Solutions for Low-Power Smart Edge Computing

• Published in: Journal of low power electronics and applications Vol. 12; no. 4; p. 61
• Main Authors: Martin Wisniewski, Lucas, Bec, Jean-Michel, Boguszewski, Guillaume, Gamatié, Abdoulaye
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2022; MDPI
• Subjects: Accuracy; Algorithms; Bandwidths; Communication; Computer centers; Computer Science; Costs; CYSmart; Decision analysis; Design; Distance learning; Edge computing; Electronic devices; Embedded systems; Energy efficiency; Energy management systems; Field programmable gate arrays; Internet of Things; Linear algebra; low-power embedded systems; Machine learning; Online instruction; Power efficiency; Power management; Sensors; smart edge computing; Surveys; smart edge computing; Internet-of-Things; low-power embedded systems; machine learning; energy-efficiency; CYSmart
• ISSN: 2079-9268; 2079-9268
• DOI: 10.3390/jlpea12040061

The edge computing paradigm for Internet-of-Things brings computing closer to data sources, such as environmental sensors and cameras, using connected smart devices. Over the last few years, research in this area has been both interesting and timely. Typical services like analysis, decision, and control, can be realized by edge computing nodes executing full-fledged algorithms. Traditionally, low-power smart edge devices have been realized using resource-constrained systems executing machine learning (ML) algorithms for identifying objects or features, making decisions, etc. Initially, this paper discusses recent advances in embedded systems that are devoted to energy-efficient ML algorithm execution. A survey of the mainstream embedded computing devices for low-power IoT and edge computing is then presented. Finally, CYSmart is introduced as an innovative smart edge computing system. Two operational use cases are presented to illustrate its power efficiency.