Ionic liquid-based reservoir computing: The key to efficient and
flexible edge computing

Date:
April 28, 2022
Source:
Tokyo University of Science
Summary:
Researchers have designed a tunable physical reservoir device based
on the dielectric relaxation at an electrode-ionic liquid interface.

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.



FULL STORY ========================================================================== 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 tunability, limiting the signals
it can process. Now, researchers from Japan present ionic liquids as
an easily tunable physical reservoir device that can be optimized to
process signals over a broad range of timescales by simply changing
their viscosity.


========================================================================== Artificial Intelligence (AI) is fast becoming ubiquitous in the modern
society and will feature a broader implementation in the coming years. In applications involving sensors and internet-of-things devices, the norm
is often edge AI, a technology in which the computing and analyses
are performed 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,
traits that are particularly desirable in processing time-series data
in real time.

In this regard, physical reservoir computing (PRC), which relies on the transient dynamics of physical systems, can greatly simplify the computing paradigm of edge AI. This is because PRC can be used to store and process analog signals into those edge AI can efficiently work with and analyze.

However, the dynamics of solid PRC systems are characterized by
specific timescales that are not easily tunable and are usually too
fast for most physical signals. This mismatch in timescales and their
low controllability make PRC largely unsuitable for real-time processing
of signals in living environments.

To address this issue, a research team from Japan involving 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." 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 (the positively charged ion) can be easily varied with the
length of a chosen alkyl chain. They 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 demonstrated 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%.

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.


========================================================================== Story Source: Materials provided by Tokyo_University_of_Science. Note:
Content may be edited for style and length.


========================================================================== Journal Reference:
1. Sang-Gyu Koh, Hisashi Shima, Yasuhisa Naitoh, Hiroyuki Akinaga,
Kentaro
Kinoshita. Reservoir computing with dielectric relaxation at an
electrode-ionic liquid interface. Scientific Reports, 2022; 12
(1) DOI: 10.1038/s41598-022-10152-9 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/04/220428085817.htm

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