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Physical tank computing can be used to perform high-speed processing for low-power artificial intelligence.

Researchers in Japan are designing an adjustable physical tank device based on dielectric relaxation at a liquid electrode-ion interface.

In the near future, more and more artificial intelligence processing will have to take place at the edge - close to the user and where data is collected, rather than on a remote computer server. This will require high-speed, low-power data processing. Calculating the physical tank is an attractive platform for this purpose, and a new discovery by Japanese scientists has just made this much more flexible and practical.

Physical Reservoir Computing (PRC), which relies on the transient response of physical systems, is an attractive machine learning framework that can perform high-speed processing of time-series signals at low power. However, PRC systems have low adjustability, limiting the signals they can process. Now, researchers in Japan are presenting ionic liquids as an easy-to-adjust physical tank device that can be optimized to process signals over a wide range of time intervals by simply changing their viscosity.

Artificial intelligence (AI) is fast becoming ubiquitous in modern society and will be widely implemented in the coming years. In applications involving sensors and internet-of-things devices, the norm is often edge AI, a technology in which computation and analysis are performed close to the user (where data is collected) and not far away, on a centralized server. This is because edge AI has low power requirements as well as high-speed data processing capabilities, which are highly desirable in real-time time series data processing.

Time scale of signals commonly produced in living environments

Time scale of signals commonly produced in living environments. The response time of the liquid ionic PRC system developed by the team can be adjusted to be optimized for processing such real-world signals. Credit: Kentaro Kinoshita of the TUS

In this sense, the calculation of the physical reservoir (PRC), which is based on the transient dynamics of physical systems, can greatly simplify the calculation paradigm of marginal AI. This is because PRC can be used to store and process analog signals at those edges with which AI can work and analyze efficiently. However, the dynamics of solid PRC systems are characterized by specific time intervals that are not easy to adjust and are usually too fast for most physical signals. This mismatch in time intervals and their low controllability make PRC largely unsuitable for real-time processing of signals in living environments.

To address this issue, a Japanese research team involving Professor Kentaro Kinoshita and Sang-Gyu Koh, a doctoral student at 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 the journal Scientific reports, the use of liquid PRC systems instead. “The replacement of conventional solid tanks with liquid ones should lead to AI devices that can learn directly on the time scale of signals generated by the environment, such as voice and vibration, in real time,” explains Prof. Kinoshita. “Ionic liquids are stable molten salts that are completely composed of free electric charges. The dielectric relaxation of the ionic liquid, or the way its charges are rearranged in response to an electrical signal, could be used as a reservoir and is very promising for peak AI computation. ”

Ionic Liquid Tank Calculation

The response of the liquid ionic PRC system can be adjusted to be optimized for the processing of a wide range of signals by changing its viscosity by adjusting the length of the cationic side chain. Credit: Kentaro Kinoshita of the TUS

In their study, 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 (positively charged ion) can be slightly varied with the length of a selected alkyl chain. They made gold electrodes and filled the gaps with IL. “We found that the time scale of the tank, although complex in nature, can be directly controlled by the viscosity of 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 wide range of computational applications in the future, ”says Prof. Kinoshita. By adjusting the length of the alkyl chain between 2 and 8 units, the researchers obtained characteristic response times ranging from 1 to 20 µs, with longer alkyl side chains leading to longer response times and adjustable AI learning performance of the devices.

System adjustability was demonstrated using an AI image identification task. AI was presented with a handwritten image as input, which was represented by rectangular pulse voltages with a width of 1 µs. By increasing the length of the side chain, the team brought the transient dynamics closer to that of the target signal, the discrimination rate improving for longer chain lengths. That’s because compared to [emim+] [TFSI]in which the current relaxed to its value in about 1 µs, IL with a longer side chain and, in turn, a longer relaxation time, better preserved the time series data history, improving the identification[{” attribute=””>accuracy. When the longest sidechain of 8 units was used, the discrimination rate reached a peak value of 90.2%.

Input Signal Conversion Through Ionic Liquid Based PRC System

Input signal conversion through the ionic liquid-based PRC system. The reservoir output in the form of current response (top and middle) to an input voltage pulse signal (bottom) are shown. If the current decay (dielectric relaxation) is too fast/slow, it reaches its saturation value before the next signal input and no history of the previous signal is retained (middle image). Whereas, if the current response attenuates with a relaxation time that is properly matched with the time scales of the input pulse, the history of the previous input signal is retained (top image). Credit: Kentaro Kinoshita from TUS

These findings are encouraging as they clearly show 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. This could pave the way for edge AI devices that can accurately learn the various signals produced in the living environment in real time.

Computing has never been more flexible!

Reference: “Reservoir computing with dielectric relaxation at an electrode–ionic liquid interface” by Sang-Gyu Koh, Hisashi Shima, Yasuhisa Naitoh, Hiroyuki Akinaga and Kentaro Kinoshita, 28 April 2022, Scientific Reports.
DOI: 10.1038/s41598-022-10152-9

Kinoshita Kentaro is a Professor at the Department of Applied Physics at Tokyo University of Science, Japan. His area of interest is device physics, with a focus on memory devices, AI devices, and functional materials. He has published 105 papers with over 1600 citations to his credit and holds a patent to his name.

This study was partly supported by JSPS KAKENHI Grant Number JP20J12046.

Tokyo University of Science (TUS) is a well-known and respected university, and the largest science-specialized private research university in Japan, with four campuses in central Tokyo and its suburbs and in Hokkaido. Established in 1881, the university has continually contributed to Japan’s development in science through inculcating the love for science in researchers, technicians, and educators.

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