Ionic Liquid-Based Reservoir Computing

By Chad Cox

Associate Editor

Embedded Computing Design

April 29, 2022


Ionic Liquid-Based Reservoir Computing

Researchers from Japan design a tunable physical reservoir device based on the dielectric relaxation at an electrode-ionic liquid interface.

Physical reservoir computing (PRC) relies on the transient response of physical systems. It is an appealing machine learning framework enabled to perform high-speed processing of time-series signals at low power.

PRC systems have minimal tunability, limiting the signals it can process. Researchers from Japan showed findings that ionic liquids, as an easily tunable physical reservoir device, can be enhanced to process signals over a broad range of timescales by merely shifting viscosity.

Within applications involving sensors and IoT devices, the norm is often edge AI. AI at the edge has low power requirements as well as high-speed data processing capabilities. Both these traits are attractive in processing time-series data in real time.

Physical reservoir computing (PRC) can greatly streamline the computing paradigm of edge AI. PRC can be used to store and process analog signals into edge AI devices. Solid PRC systems are characterized by specific timescales that are not easily tunable and usually too fast for most physical signals. The mismatch in timescales and low controllability make PRC largely unsuitable for real-time processing of signals in living environments.

A research team from Japan that involved Professor Kentaro Kinoshita and Sang-Gyu Koh, a PhD student, from the Tokyo University of Science, and senior researchers Dr. Hiroyuki Akinaga, Dr. Hisashi Shima, and Dr. Yasuhisa Naitoh from the National Institute of Advanced Industrial Science and Technology, proposed, in a new study published in Scientific Reports, the use of liquid PRC systems instead. “Replacing conventional solid reservoirs with liquid ones should lead to AI devices that can directly learn at the time scales of environmentally generated signals, such as voice and vibrations, in real time,” explains Prof. Kinoshita. “Ionic liquids are stable molten salts that are completely made up of free-roaming electrical charges. The dielectric relaxation of the ionic liquid, or how its charges rearrange as a response to an electric signal, could be used as a reservoir and is holds much promise for edge AI physical computing.”

The team designed a PRC system with an ionic liquid (IL) of an organic salt, 1-alkyl-3-methylimidazolium bis(trifluoromethane sulfonyl)imide ([Rmim+] [TFSI-] R = ethyl (e), butyl (b), hexyl (h), and octyl (o)), whose cationic part (the positively charged ion) can be easily varied with the length of a chosen alkyl chain.

The team fabricated gold gap electrodes, and filled in the gaps with the IL. “We found that the timescale of the reservoir, while complex in nature, can be directly controlled by the viscosity of the IL, which depends on the length of the cationic alkyl chain. Changing the alkyl group in organic salts is easy to do, and presents us with a controllable, designable system for a range of signal lifetimes, allowing a broad range of computing applications in the future,” says Prof. Kinoshita. By adjusting the alkyl chain length between 2 and 8 units, the researchers achieved characteristic response times that ranged between 1 – 20 ms, with longer alkyl sidechains leading to longer response times and tunable AI learning performance of devices.

The tunability of the system was revealed using an AI image identification task. The AI was presented a handwritten image as the input, which was represented by 1 ms width rectangular pulse voltages. By increasing the side chain length, the team made the transient dynamics approach that of the target signal, with the discrimination rate improving for higher chain lengths. This is because, compared to [emim+] [TFSI-], in which the current relaxed to its value in about 1 ms, the IL with a longer side chain and, in turn, longer relaxation time retained the history of the time series data better, improving identification accuracy. When the longest sidechain of 8 units was used, the discrimination rate reached a peak value of 90.2%.

The findings are promising as the demonstration showed that the proposed PRC system based on the dielectric relaxation at an electrode-ionic liquid interface can be suitably tuned according to the input signals by simply changing the IL’s viscosity, paving the way for edge AI devices that can accurately learn the various signals produced in the living environment in real time.

For the complete paper, visit


Authors: Sang-Gyu Koh1, 2, Hisashi Shima2, Yasuhisa Naitoh2, Hiroyuki Akinaga2, and Kentaro Kinoshita1

Title of original paper: Reservoir computing with dielectric relaxation at an electrode– ionic liquid interface

Journal: Scientific Reports