A Clockless Derivative-Dependent Sampling Scheme for Energy-Efficient IoT Applications

This article presents a clockless nonuniform sampling scheme to enhance the energy efficiency of Internet of Things (IoT) applications. The proposed scheme employs a derivative-dependent mechanism that provides enhanced accuracy compared to other nonuniform sampling schemes while minimizing power consumption. By continuously monitoring the change in the derivative of the input signal, the proposed scheme identifies the most significant points of the signal, valuable for retention and conversion for effective signal reconstruction. In this scheme, the change in the derivative of the signal is compared to tunable threshold references, enabling adjustability to obtain the desired level of accuracy and adaptability to a variety of IoT applications. The proposed scheme is implemented in low- and high-speed systems that target low- and high-frequency applications, respectively. Fabricated using TSMC’s 0.13- $\mu $ m CMOS technology, the performance is evaluated through experimental results in real-world scenarios. The proposed clockless derivative dependent sampling (CL-DDS) system can be integrated into the data acquisition system of an IoT device/sensor to save its critical power budget, while the threshold references are tuned to achieve the desired accuracy. The maximum power consumption of the proposed low- and high-speed CL-DDS designs is $1.15~\mu $ W (@1 MHz) and $8.81~\mu $ W (@20 MHz), respectively.