EPSRC logo

Details of Grant 

EPSRC Reference: EP/T027479/1
Title: Oxide Recurrent Neural Networks
Principal Investigator: Borisov, Dr P
Other Investigators:
Researcher Co-Investigators:
Project Partners:
ARM Ltd
Department: Physics
Organisation: Loughborough University
Scheme: New Investigator Award
Starts: 01 June 2020 Ends: 31 May 2023 Value (£): 277,037
EPSRC Research Topic Classifications:
Electronic Devices & Subsys.
EPSRC Industrial Sector Classifications:
Information Technologies Electronics
Related Grants:
Panel History:
Panel DatePanel NameOutcome
03 Mar 2020 EPSRC ICT Prioritisation Panel March 2020 Announced
Summary on Grant Application Form
The human brain masters the tasks of associative memory, object detection, body movement, speech and text recognition and classification, which are still challenging for the standard computer software to tackle. Brain-like, that is, neuromorphic algorithms have been developed to deal with the aforementioned problems.

The main component is the neural network of artificial neurons that are linked together by connection weights which can be "trained" to deliver the desired set of outputs, for example, to assign a meaning to a spoken word. The software that does "learning by doing" is one of the major constituents of the recent rapid development in the field of artificial intelligence technology, which has considerably improved the cognitive functionality of modern electronics, for example, face and speech recognition in smartphones.

But still, even though an average computer can process by far much more mathematical operations per second than the human brain, when it comes to the energy efficiency the human brain is the clear winner, being by several orders of magnitude more energy efficient. In particular, the so-called recurrent neural networks that were developed for processing signals in form of temporal sequences and do include the prehistory of the signal into computations, represent a complicated case in terms of energy-efficiency, which is highly desirable in mobile or always-on applications. The sequential nature of temporal signals makes it difficult to minimise energy costs due to weights storage and repeated vector-matrix multiplication.

There is already some success in using thin non-stoichiometric oxide films that demonstrate variable electric resistance, the so-called memristors, for performing parallel computations of vector-matrix products at low energy costs. Arrays of memristors prepared between sets of metallic crossbar electrodes can be implemented for image recognition in neural networks, with electric conductance values of memristors representing the corresponding weights.

We propose (i) to prepare and characterise thin, defect-rich oxide films that will play the role of a physical substrate where the incoming signals will be transformed in accordance with their prehistory, re-routed, and re-mixed to the output signals. (ii) to incorporate those films into prototypes of new type hardware operating by the approach of recurrent neural networks and being able to process, i.e. generate, predict or classify time-dependant signals typical for different medical, engineering and environmental monitoring sensors, robotics control, video and audio recordings; (iii) to test and benchmark those device prototypes in terms of performance and energy efficiency with respect to a set of tasks, for example, spoken word recognition.

Key Findings
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Potential use in non-academic contexts
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Impacts
Description This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Summary
Date Materialised
Sectors submitted by the Researcher
This information can now be found on Gateway to Research (GtR) http://gtr.rcuk.ac.uk
Project URL:  
Further Information:  
Organisation Website: http://www.lboro.ac.uk