In-materio reservoir computing based on nanowire networks: fundamental, progress, and perspective

Abstract

The reservoir computing (RC) system, known for its ability to seamlessly integrate memory and computing functions, is considered as a promising solution to meet the high demands for time and energy-efficient computing in the current big data landscape, compared with traditional silicon-based computing systems that have a noticeable disadvantage of separate storage and computation. This review focuses on in-materio RC based on nanowire networks (NWs) from the perspective of materials, extending to reservoir devices and applications. The common methods used in preparing nanowires-based reservoirs, including the synthesis of nanowires and the construction of networks, are firstly systematically summarized. The physical principles of memristive and memcapacitive junctions are then explained. Afterwards, the dynamic characteristics of nanowires-based reservoirs and their computing capability, as well as the neuromorphic applications of NWs-based RC systems in recognition, classification, and forecasting tasks, are explicated in detail. Lastly, the current challenges and future opportunities facing NWs-based RC are highlighted, aiming to provide guidance for further research.

An organized view of reservoir computing: a perspective on theory and technology development

“…The Japan Society of Applied Physics by IOP Publishing Ltd network adaptability between ordered and chaotic dynamics, has been widely investigated. 18,60,[103][104][105][106][107][108] Moreover, the integration of POM-decorated single-walled carbon nanotubes (POM-SWCNTs) in neuromorphic devices for computing has demonstrated the use of multiple redox states of POMs and electrochemical reactions to generate spontaneous spikes and noise, which can result in improved performance of PRC (Fig. 13).…”

“…Despite superior computing merits, the application of NWbased RC to real-life applications requires continuous efforts due to existing challenges such as material compatibility, the dynamic complexity of traditional CMOS technology, and achieving optimal performance. 18)…”

“…Despite architectural differences, both methods leverage reservoirs of neurons to effectively address time-series problems, leading to their classification under RC. 18) The concept of an LSM (or echo state machine) helps us to both understand and to harness the role of stochasticity and dynamics for computation.…”

Oxide‐Based Electrolyte‐Gated Transistors with Stable and Tunable Relaxation Responses for Deep Time‐Delayed Reservoir Computing

“…Traditional RC systems typically require a large number of elements and then face significant volume and energy costs. [7,8] In recent decades, intensive attention has been paid to timedelayed RC, which consists of single nonlinear physical node and a delayed feedback loop containing several virtual nodes segmented through time multiplexing. [9] This contributes to a more friendly hardware implementation by reducing the number of physical devices and thus lowering computing costs.…”

Dynamic memristor for physical reservoir computing

“…Benefiting from the inherent properties of RC, it effectively diminishes the necessity for extensive training, thus significantly lowering the cost of hardware learning. A variety of PRC system implementations with different mechanisms enrich the computing resources, including self-organized nanowire PRC, 25 logic circuits PRC, 26 magnon PRC, 27 and memristor PRC, 28 etc . It is worth noting that the memristor fabrication process is compatible with the existing complementary metal oxide semiconductor (CMOS) processes, 29 thus attracting a significant amount of attention.…”