Design Strategies and Applications of Reservoir Computing: Recent Trends and Prospects [Feature]

Abstract

Reservoir computing (RC) is a neural computing paradigm especially well-suited for learning dynamical systems by leveraging an untrained reservoir layer, providing high-dimensional input encoding with fading memory property. Since only the readout weights are trained under RC, linear regression learning algorithms are sufficient, leading to significant improvements in computational complexity and energy efficiency as compared to other deep neural networks (DNNs). RC offers an alternative solution to sidestep the shortcomings of data scarcity and the vanishing gradient problem. More importantly, such a network structure is amenable to hardware implementation using a variety of devices, circuits, and systems, making RC a good candidate to replace sophisticated DNNs as a lightweight classifier at the edge for internet of things (IoT) applications. In this article, we provide an overview of recent advances in RC hardware and their applications for mobile edge intelligence. Specifically, we will demonstrate the design strategies of RC in opto-electronic configuration, fully digital system, and silicon with the mixed-signal integrated circuit approach. Moreover, we will expose a novel implementation approach using emerging materials, designing the way for RC to be used in the next-generation neuromorphic computing systems. Building upon these efficient RC models, their applicability and effectiveness against the state-of-the-art are then demonstrated through diverse machine learning benchmarks spanning the area of IoT, communication networks, and healthcare.