Reservoir Computing for Scalable Hardware with Block‐Based Neural Network

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.