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Details of Grant 

EPSRC Reference: EP/N035127/1
Title: LAMBDA: Learning Algorithms for Modularity in Broad and Deep Architectures
Principal Investigator: Brown, Professor G
Other Investigators:
Lujan, Professor M
Researcher Co-Investigators:
Project Partners:
ARM Ltd
Department: Computer Science
Organisation: University of Manchester, The
Scheme: Standard Research
Starts: 01 September 2016 Ends: 31 August 2020 Value (£): 521,154
EPSRC Research Topic Classifications:
Artificial Intelligence Computer Sys. & Architecture
EPSRC Industrial Sector Classifications:
Electronics Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
28 Apr 2016 EPSRC ICT Prioritisation Panel - Apr 2016 Announced
Summary on Grant Application Form
Do you know how Facebook recognises faces in images? Do you know how your iPhone understands your speech? The secret behind each of these is a technology called "Deep Learning", which uses biologically-inspired algorithms called "neural networks".

Over the next decade, society will become more reliant on this technology. But... these algorithms require an IMMENSE amount of computing power, and therefore electricity, and for example can take many weeks to learn a given task.

The LAMBDA project explores an approach to deep learning which is not just deep, but also broad - hence "Learning Algorithms for Broad and Deep Architectures". We aim to (1) make "broad" models that are faster/easier to learn, and as a consequence (2) reduce the energy consumption. Our approach builds upon previous award winning research by the PI, in exactly this area. If successful, we will be able to reproduce the same abilities as current deep neural networks, but with a significantly reduced energy consumption, and whilst learning such architectures a significantly easier task for scientists.

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Organisation Website: http://www.man.ac.uk