Efficient Design of Hardware-Enabled Reservoir Computing in FPGAs

TL;DR

This paper presents a novel approach for designing hardware-efficient reservoir computing systems in FPGAs by combining data-adaptive input masking with genetic algorithm optimization, enabling self-adaptive, high-speed machine learning hardware.

Contribution

It introduces a data-structure-aware input mask adaptation and an evolutionary optimization method for reservoir computing hardware, reducing design effort and improving efficiency.

Findings

Effective reservoir input mask adaptation via autoencoders

Genetic algorithms significantly reduce optimization evaluations

Hardware implementation on FPGAs confirms practical viability

Abstract

In this work, we propose a new approach towards the efficient optimization and implementation of reservoir computing hardware reducing the required domain expert knowledge and optimization effort. First, we adapt the reservoir input mask to the structure of the data via linear autoencoders. We therefore incorporate the advantages of dimensionality reduction and dimensionality expansion achieved by conventional algorithmically efficient linear algebra procedures of principal component analysis. Second, we employ evolutionary-inspired genetic algorithm techniques resulting in a highly efficient optimization of reservoir dynamics with dramatically reduced number of evaluations comparing to exhaustive search. We illustrate the method on the so-called single-node reservoir computing architecture, especially suitable for implementation in ultrahigh-speed hardware. The combination of both methods and the resulting reduction of time required for performance optimization of a hardware system establish a strategy towards machine learning hardware capable of self-adaption to optimally solve specific problems. We confirm the validity of those principles building reservoir computing hardware based on a field-programmable gate array.