Efficient and Flexible Edge Computing

Flashing Technology Computer Concept

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

Researchers from Japan are designing a customizable physical reservoir device based on dielectric relaxation at an electrode-ion liquid interface.

In the near future, more and more artificial intelligence will have to take place on the edge – close to the user and where the data is collected rather than on a remote computer server. This will require high-speed data processing with low power consumption. Physical reservoir computing is an attractive platform for this purpose, and a new success of scientists in Japan has recently made this much more flexible and practical.

Physical reservoir computing (PRC), which depends 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 configurability, limiting the signals it can process. Researchers in Japan now present ionic liquids as an easily configurable physical reservoir that can be optimized to process signals over a wide range of time scales simply by changing their viscosity.

Artificial Intelligence (AI) is fast becoming ubiquitous in modern society and will have a wider implementation in the coming years. In applications involving sensors and Internet devices, the standard is often edge AI, a technology in which the computing and analysis is done close to the user (where the 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, features that are especially desirable in real-time data-processing.

Time Scale of Signals Often Produced in Living Environments

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

In this regard, physical reservoir computing (PRC), which depends on the transient dynamics of physical systems, can greatly simplify the computational paradigm of edge AI. This is because PRC can be used to store and process analog signals in those edge AI with which it can effectively work and analyze. However, the dynamics of solid PRC systems are characterized by specific time scales that are not easily configurable and are usually too fast for most physical signals. This mismatch in time scales and their low controllability make PRC largely irrational for real-time signal processing in habitats.

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 of 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. “Replacing conventional solid reservoirs with liquids should lead to AI devices that can directly learn on the time scales of environmentally generated signals, such as voice and vibration, in real time,” explains Prof. Kinoshita. “Ionic liquids are stable molten salts that consist entirely of free-flowing electrical charges. The dielectric relaxation of the ionic liquid, or how its charges are rearranged in response to an electrical signal, could be used as a reservoir and has much promise for edge AI physical computing. “

Ionic Liquid Based Reservoir Computing

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

In their study, the team designed a PRC system with an ionic liquid (IL) of 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 according to the length of a selected alkyl chain. They made gold gap electrodes, and filled the gap with the IL. “We have found that the time scale of the reservoir, although 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 into organic salts is easily feasible, and presents to “We have a controllable, designable system for a range of signal lifetimes, allowing for a wide range of computer applications in the future,” says Prof. Kinoshita. longer alkyl side chains leading to longer response times and customizable AI learning devices.

The configurability of the system was demonstrated using an AI image identification task. The AI ​​was presented with a handwritten image as the input, which was represented by 1 µs wide rectangular pulse extensions. By increasing the side chain length, the team made the transient dynamic approach closer to that of the target signal, with the discrimination rate improving for higher chain lengths. This is because, compared to [emim+] [TFSI]in which the flow relaxed to its value in about 1 µs, the IL with a longer side chain and, in turn, a longer relaxation time retained the history of the time series data better, improving 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.

Leave a Reply

Your email address will not be published.