Nanomechanical Systems for Reservoir Computing Applications

Abstract

Reservoir computing (RC) provides a route to use physical systems for computation and machine learning. Owing to their inherent nonlinearity, nanomechanical systems constitute an interesting technology to serve as reservoir. While RC platforms are built using microelectromechanical systems, the energy efficiency, response time, and footprint of these systems can be significantly improved by using nanoscale devices. Herein, the use of nanoelectromechanical systems (NEMS) is investigated, which can be used in RC, utilizing inherent nonlinearities and the fading memory effect from the transient response of NEMS. The smaller size and higher operating frequencies of NEMS enable faster processing rates compared to micromechanical systems, while their compact footprint, low power consumption, and ability to operate under ambient conditions simplify integration into practical applications. In modified national institute of standards and technology (MNIST) handwritten digit–recognition test, this system achieves 90% accuracy with a 3.3 μs processing time per pixel. Also the effect of driving frequency and amplitude on NEMS classification accuracy is investigated using experiments and simulations and it is shown that no significant dependency in any of the parameters is observed. Herein, an estimate for energy consumption of core NEMS RC system on MNIST data is provided. These results highlight the potential for various applications that require efficient and fast information processing in resource-constrained environments.