Reservoir Computing for Scalable Hardware with Block‐Based Neural Network
Kundo Lee, Tomoki Hamagami · IEEJ Transactions on Electrical and Electronic Engineering · 2021
Abstract This paper presents a reservoir computing technique using Asynchronous Block‐Based Neural Networks (ABBNN). Echo State Networks and Liquid State Machines introduced a new paradigm in artificial neural networks (ANN), known as Reservoir Computing (RC). A recurrent neural network (RNN) in Reservoir Computing is generated randomly, and only a readout is trained. The reservoir computing greatly facilitated the practical RNN application and outperformed classical RNN in many tasks. ABBNN is an extended version of the classical block‐based neural network model. ABBNN, an evolvable neural network model, provides a model‐free estimation of nonlinear dynamical systems. To propose a hardware‐aware model of reservoir computing and high‐speed training, we introduce Block‐Based Reservoir Computing (BBRC). BBRC provides a flexible architecture. The architecture based on the basic blocks provides a regularity that supports scalable hardware. In contrast, BBRC also supports randomness based on the internal configuration. Both are significant features in achieving an optimal hardware reservoir computer. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.