Enhancing Performance of Reservoir Computing System Based on Coupled MEMS Resonators

Abstract

Reservoir computing (RC) is an attractive paradigm of a recurrent neural network (RNN) architecture, owning to the ease of training and existing neuromorphic implementation. Its simulated performance matches other digital algorithms on a series of benchmarking tasks, such as prediction tasks and classification tasks. In this article, we propose a novel RC structure based on the coupled MEMS resonators with the enhanced dynamic richness to optimize the performance of the RC system both on the system level and data set level. Moreover, we first put forward that the dynamic richness of RC comprises linear dynamic richness and nonlinear dynamic richness, which can be enhanced by adding delayed feedbacks and nonlinear nodes, respectively. In order to set forth this point, we compare three typical RC structures, a single-nonlinearity RC structure with single-feedback, a single-nonlinearity RC structure with double-feedbacks, and the couple-nonlinearity RC structure with double-feedbacks. Specifically, four different tasks are enumerated to verify the performance of the three RC structures, and the results show the enhanced dynamic richness by adding delayed feedbacks and nonlinear nodes. These results prove that coupled MEMS resonators offer an interesting platform to implement a complex computing paradigm leveraging their rich dynamical features.

Processing IMU action recognition based on brain-inspired computing with microfabricated MEMS resonators

“…Articles using this data set [20,40] only arrange each pixel in order and then input them in sequence, which does not mean that there is an actual time-domain correlation between pixels. Although the KTH video data set [34,39] is used to identify time-related image frame information, the pixel point of each frame image is also time-independent. Therefore, we need to design a data set closely related to timing information in order to use MEMS resonators for processing.…”

“…Due to its excellent Duffing nonlinear performance and suitable attenuation characteristics, MEMS resonators are appropriate as the nonlinear node of the hardware reservoir system [31,32]. Most of the prediction and classification tasks processed by hardware RC are based on the general data sets, such as parity benchmark [30], nonlinear autoregressive moving average (NARMA) task [13,33,34], Santa Fe laser [26,35,36], Mackey-Glass time-series tasks [35], nonlinear channel equalization benchmark task [37,38], signal classification [18,26], isolated word recognition [13,18,30], video action recognition [34,39], and handwritten digit classification [20,40]. But so far, there is no research on using hardware RC systems to process human action recognition data sets with timeindependent information.…”

Design of Tone Recognition System for Pipa Strings Based on Wireless Sensors

“…The vivid expression and complete display of the charm of music can provide the listeners with a flawless experience and sensation that is more conducive to stimulating their emotional resonance. It also significantly boosts the overall effect of the pipa performance [7][8][9].…”

Piezoelectric MEMS-based physical reservoir computing system without time-delayed feedback

“…Subsequently, various MEMS-based RC systems, including neuromorphic accelerometers, have been proposed. [32][33][34][35][36][37][38] To obtain sufficient short-term memory characterisitics, most of these systems employ time-delayed feedback and additional masking procedures. In the former, the output from the reservoir layer is delayed for a certain period of time and then input to the reservoir layer again.…”