MEMS reservoir computing system with stiffness modulation for multi-scene data processing at the edge  

作　　者：Xiaowei Guo Wuhao Yang Xingyin Xiong Zheng Wang Xudong Zou 

机构地区：[1]The State Key Laboratory of Transducer Technology,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing,China [2]School of Electronic,Electrical and Communication Engineering,University of Chinese Academy of Sciences,Beijing,China [3]QILU Aerospace Information Research Institute,Jinan,China

出　　处：《Microsystems & Nanoengineering》2024年第3期421-432,共12页微系统与纳米工程（英文）

基　　金：supported by the National Natural Science Foundation of China(Grant No.61971399);the Key Research Program of Frontier Science(CAS,Grant No.ZDBS-LY-JSC028).

摘　　要：Reservoir computing(RC)is a bio-inspired neural network structure which can be implemented in hardware with ease.It has been applied across various fields such as memristors,and electrochemical reactions,among which the microelectro-mechanical systems(MEMS)is supposed to be the closest to sensing and computing integration.While previous MEMS RCs have demonstrated their potential as reservoirs,the amplitude modulation mode was found to be inadequate for computing directly upon sensing.To achieve this objective,this paper introduces a novel MEMS reservoir computing system based on stiffness modulation,where natural signals directly influence the system stiffness as input.Under this innovative concept,information can be processed locally without the need for advanced data collection and preprocessing.We present an integrated RC system characterized by small volume and low power consumption,eliminating complicated setups in traditional MEMS RC for data discretization and transduction.Both simulation and experiment were conducted on our accelerometer.We performed nonlinearity tuning for the resonator and optimized the post-processing algorithm by introducing a digital mask operator.Consequently,our MEMS RC is capable of both classification and forecasting,surpassing the capabilities of our previous non-delay-based architecture.Our method successfully processed word classification,with a 99.8%accuracy,and chaos forecasting,with a 0.0305 normalized mean square error(NMSE),demonstrating its adaptability for multi-scene data processing.This work is essential as it presents a novel MEMS RC with stiffness modulation,offering a simplified,efficient approach to integrate sensing and computing.Our approach has initiated edge computing,enabling emergent applications in MEMS for local computations.

关 键 词：COMPUTING HARDWARE STIFFNESS 

分 类 号：TP333[自动化与计算机技术—计算机系统结构]