Sun Jie, Yang Wuhao, Zheng Tianyi, Xiong Xingyin, Guo Xiaowei, Zou Xudong
The State Key Laboratory of Transducer Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China.
Micromachines (Basel). 2022 Feb 18;13(2):317. doi: 10.3390/mi13020317.
Reservoir computing (RC) is a potential neuromorphic paradigm for physically realizing artificial intelligence systems in the Internet of Things society, owing to its well-known low training cost and compatibility with nonlinear devices. Micro-electro-mechanical system (MEMS) resonators exhibiting rich nonlinear dynamics and fading behaviors are promising candidates for high-performance hardware RC. Previously, we presented a non-delay-based RC using one single micromechanical resonator with hybrid nonlinear dynamics. Here, we innovatively introduce a nonlinear tuning strategy to analyze the computing properties (the processing speed and recognition accuracy) of the presented RC. Meanwhile, we numerically and experimentally analyze the influence of the hybrid nonlinear dynamics using the image classification task. Specifically, we study the transient nonlinear saturation phenomenon by fitting quality factors under different vacuums, as well as searching the optimal operating point (the edge of chaos) by the static bifurcation analysis and dynamic vibration numerical models of the Duffing nonlinearity. Our results in the optimal operation conditions experimentally achieved a high classification accuracy of (93 ± 1)% and several times faster than previous work on the handwritten digits recognition benchmark, profit from the perfect high signal-to-noise ratios (quality factor) and the nonlinearity of the dynamical variables.
储层计算（RC）是一种潜在的神经形态范式，由于其众所周知的低训练成本以及与非线性器件的兼容性，有望在物联网社会中物理实现人工智能系统。具有丰富非线性动力学和衰落行为的微机电系统（MEMS）谐振器是高性能硬件RC的有前途的候选者。此前，我们提出了一种基于单个具有混合非线性动力学的微机械谐振器的无延迟RC。在此，我们创新性地引入一种非线性调谐策略来分析所提出的RC的计算特性（处理速度和识别精度）。同时，我们使用图像分类任务对混合非线性动力学的影响进行了数值和实验分析。具体而言，我们通过拟合不同真空度下的品质因数来研究瞬态非线性饱和现象，并通过达夫ing非线性的静态分岔分析和动态振动数值模型来搜索最佳工作点（混沌边缘）。我们在最佳操作条件下的结果在手写数字识别基准上实验实现了（93±1）%的高分类准确率，并且比之前的工作快几倍，这得益于完美的高信噪比（品质因数）和动态变量的非线性。
