A sensor system integrating sensing and intelligence based on MEMS reservoir computing

Abstract

Reservoir computing (RC) is a bio-inspired neural network structure which is easy to be implemented in hardware. It has been constructed in a great many fields such as memristor, electrochemical reaction, among which MEMS is the closest to integrate sensing and computing. We propose a novel sensor system of MEMS RC based on stiffness modulation, that natural signal directly affects the system stiffness as the input. Under this paradigm, information can be processed locally without data collection and pre-processing. We inherited the nonlinearity tuning principle and optimized the post-processing algorithm by creating a digital mask operator. In this way, the system can deal with classification tasks as well as forecasting tasks. We integrated MEMS, IC and FPGA with a small volume and low power consumption, so complicated setup for data discretization and transduction in traditional MEMS RC is eliminated. The system can process word classification and chaos forecasting with high accuracy, which preliminarily proves the integrated RC architecture.