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Details of Grant 

EPSRC Reference: EP/X017001/1
Title: Beyond neuromorphic: Exploiting the extended frequency response of memristive devices and systems to process information in new ways.
Principal Investigator: Kenyon, Professor AJ
Other Investigators:
Researcher Co-Investigators:
Project Partners:
Department: Electronic and Electrical Engineering
Organisation: UCL
Scheme: Standard Research - NR1
Starts: 01 October 2022 Ends: 31 March 2024 Value (£): 202,082
EPSRC Research Topic Classifications:
Electronic Devices & Subsys.
EPSRC Industrial Sector Classifications:
Electronics
Related Grants:
Panel History:
Panel DatePanel NameOutcome
21 Jun 2022 New Horizons 2021 Full Proposal Panel Announced
22 Jun 2022 New Horizons Electronics and Devices Panel June 2022 Announced
Summary on Grant Application Form
We propose to establish a wholly new direction in computing hardware. We will develop devices and circuits that initially take inspiration from the way the brain processes information (so-called neuromorphic computing), but will go beyond this to develop a radical new approach that uses the dynamics of the frequency response of neuromorphic devices known as memristors to process information in novel and powerful ways. Such devices are currently used as variable electrical resistors whose resistance depends on their past history. In this work we will exploit their rich dynamics by using their complex frequency response to process signals in the frequency domain and to modify the behaviour of other coupled devices in simple circuits.

This work promises to open up a new direction for memristor research and address a pressing issue for modern computing systems: their increasingly unsustainable energy demands. Given that a single state-of-the-art machine learning system can generate as much CO2 during training as five cars emit over their lifetime, and that global data centres currently consume around 250TWh per year, new low-power computing approaches are needed urgently. Neuromorphic systems take inspiration from biology to close the six order of magnitude power consumption gap between the brain and digital computing systems. In this work we will go further and add capabilities not found in biology: processing using modification of the complex frequency response of systems. This project will establish a key toolbox of novel devices to underpin next generation neuromorphic and "neuromorphic plus" computing systems.
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