Neuromorphic engineering — A paradigm shift for future IM technologies

Recent developments in measurement science have mainly focused on enhancing the quality of measurement procedures, improving their efficiency, and introducing processes that can increase accuracy [1]. Simultaneously, the implementation of intelligent and adaptive distributed systems using the Internet of Things (IoT) has recently seen a rapid increase. The synergy of these developments requires an extensive use of sensing systems that can measure, analyze and provide accurate results in real-time [2]. However, an increase in the number of sensors leads to challenges such as a necessity for complex processing strategies to handle multivariate data that further leads to overall increases in power consumption and output latency.