Ultra-Low-Power IoT applications based on Real-Time Machine Vision and Image Analysis

This period of rapid expansion in wireless communications is expected to continue. Numerous studies on CMOS RF front-end circuits have been conducted in response to the need for a cheap but effective wireless front-end. The end game is to create a design that requires as little power as possible while still delivering optimal performance. Here, we detail an ultra-low-power microsensor array for volatile chemicals, with the goal of improving its selectivity and sensitivity. Ultra-low-power Field-Programmable-Gate-Arrays (FPGAs) are a very promising solution for the energy-efficient and adaptable hardware needed for the rapid growth of the Internet of Things. In this work, we present a low-power temperature-sensing circuit operating in the time-domain for use in the Internet of Things. In this study, we investigate how to create a machine vision system for medical imaging that operates in real time. Although much effort has been put into optimising the power efficiency of the RX's individual sub-blocks, further reduction of its power allocation becomes more difficult as certain key RF blocks [i.e., low-noise amplifier (LNA) and local oscillator (LO)] consume the vast majority of the power budget in order to meet the stringent sensitivity and linearity requirements. The low intrinsic gain and high threshold voltage of the nanometer-scale CMOS area are also barriers to innovation at the circuit level.