High-order polynomial activation function and regenerative internal weights for FPGA implementation of reservoir computing

Abstract

Reservoir Computing (RC) is a machine learning framework inspired by nonlinear science, and expected to provide a solution for edge computing, owing to its simple algorithms. Therefore, the development of edge-implementable RC enables fast and lightweight information processing in edge applications. This report introduces two technologies for achieving resource efficiency and expanding nonlinear capacity in FPGA implementations of reservoir computing. We report the effect of the proposed technologies and implemented architecture and checked its architectural features and benchmark scores. In addition, as an application demonstration, we applied our system to the prediction of a chaotic dynamical system.