Edge processing research takes discovery closer to use in artificial intelligence networks

Date:
January 18, 2022
Source:
University of Surrey
Summary:
Researchers have successfully demonstrated proof-of-concept of using
their multimodal transistor (MMT) in artificial neural networks,
which mimic the human brain. This is an important step towards
using thin-film transistors as artificial intelligence hardware
and moves edge computing forward, with the prospect of reducing
power needs and improving efficiency, rather than relying solely
on computer chips.



FULL STORY ========================================================================== Researchers at the University of Surrey have successfully demonstrated
proof- of-concept of using their multimodal transistor (MMT) in artificial neural networks, which mimic the human brain. This is an important step
towards using thin-film transistors as artificial intelligence hardware
and moves edge computing forward, with the prospect of reducing power
needs and improving efficiency, rather than relying solely on computer
chips.


==========================================================================
The MMT, first reported by Surrey researchers in 2020, overcomes
long-standing challenges associated with transistors and can perform the
same operations as more complex circuits. This latest research, published
in the peer-reviewed journal Scientific Reports, uses mathematical
modelling to prove the concept of using MMTs in artificial intelligence systems, which is a vital step towards manufacturing.

Using measured and simulated transistor data, the researchers show
that well- designed multimodal transistors could operate robustly as
rectified linear unit-type (ReLU) activations in artificial neural
networks, achieving practically identical classification accuracy as
pure ReLU implementations.

They used both measured and simulated MMT data to train an artificial
neural network to identify handwritten numbers and compared the results
with the built-in ReLU of the software. The results confirmed the
potential of MMT devices for thin-film decision and classification
circuits. The same approach could be used in more complex AI systems.

Unusually, the research was led by Surrey undergraduate Isin Pesch, who
worked on the project during the final year research module of her BEng
(Hons) in Electronic Engineering with Nanotechnology. Covid meant she
had to study remotely from her home in Turkey, but she still managed to spearhead the development, complemented by an international research
team, which also included collaborators in the University of Rennes,
France and UCL, London.

Isin Pesch, lead author of the paper, which was written before
she graduated in July 2021, said: "There is a great need for
technological improvements to support the growth of low cost, large
area electronics which were shown to be used in artificial intelligence applications. Thin-film transistors have a role to play in enabling
high processing power with low resource use. We can now see that MMTs,
a unique type of thin-film transistor, invented at the University of
Surrey, have the reliability and uniformity needed to fulfil this role."
Dr Radu Sporea, Senior Lecturer at the University of Surrey's Advanced Technology Institute, said: "These findings are a reminder of how
Surrey is a leader in AI research. Many of my colleagues focus on people-centred AI and how best to maximise the benefits for humans,
including how to apply these new concepts ethically. Our research
at the Advanced Technology Institute takes forward the physical
implementation, as a stepping stone towards powerful yet affordable
next- generation hardware. It's fantastic that collaboration is
resulting in such successes with researchers involved at all levels, from undergraduates like Isin when she led this research, to seasoned experts." ========================================================================== Story Source: Materials provided by University_of_Surrey. Note: Content
may be edited for style and length.


========================================================================== Journal Reference:
1. Isin Surekcigil Pesch, Eva Bestelink, Olivier de Sagazan, Adnan
Mehonic,
Radu A. Sporea. Multimodal transistors as ReLU activation functions
in physical neural network classifiers. Scientific Reports, 2022;
12 (1) DOI: 10.1038/s41598-021-04614-9 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/01/220118104126.htm

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