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Details of Grant 

EPSRC Reference: EP/Y029763/1
Title: AI for Productive Research & Innovation in eLectronics (APRIL) Hub
Principal Investigator: Prodromakis, Professor T
Other Investigators:
Hall, Professor Dame W de Souza, Professor MM Cole, Dr J
Georgiev, Professor VP Rajendran, Professor B O'Boyle, Professor M
Eder, Professor K O'Neill, Professor M Shafik, Professor R
Ramamoorthy, Professor S Bouganis, Dr CS
Researcher Co-Investigators:
Project Partners:
AMD (Advanced Micro Devices) UK ANSYS Arc Instruments
BAE Systems Broadex Technologies UK Ltd Cadence Design Systems
Cirrus Logic (UK) Embecosm Ltd. Intel Corporation Ltd
JEOL Keysight Technologies Inc Leonardo UK ltd
MathWorks Mind Foundry Ltd Park Systems UK Limited
Pragmatic Semiconductor Limited Samsung Electronics UK Ltd Siemens
ST Microelectronics STFC Laboratories (Grouped) Synopsys (Northern Europe Ltd.)
Tessolve Thales Ltd Thermo Fisher Scientific
Department: Sch of Engineering
Organisation: University of Edinburgh
Scheme: Standard Research
Starts: 01 February 2024 Ends: 31 January 2029 Value (£): 10,274,262
EPSRC Research Topic Classifications:
Artificial Intelligence Networks & Distributed Systems
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Information Technologies
Related Grants:
Panel History:
Panel DatePanel NameOutcome
08 Nov 2023 AI for Science or Real Data Interview Panel C Announced
Summary on Grant Application Form
Artificial intelligence (AI) is undergoing an era of explosive growth. With increasingly capable AI agents such as chatGPT, AlphaFold, Gato and DALL-E capturing the public imagination, the potential impact of AI on modern society is becoming ever clearer for all to see. APRIL is a project that seeks to bring the benefits of AI to the electronics industry of the UK. Specifically, we aspire developing AI tools for cutting development times for everything from new, fundamental materials for electronic devices to complicated microchip designs and system architectures, leading to faster, cheaper, greener and overall, more power-efficient electronics.

Imagine a future where extremely complex and intricate material structures, far more complex than what a human could design alone, are optimised by powerful algorithms (such as an AlphaFold for semiconductor materials). Or consider intelligent machines with domain-specialist knowledge (think of a Gato-like system trained on exactly the right milieu of skills) experimenting day and night with manufacturing techniques to build the perfect electronic components. Or yet what if we had algorithms trained to design circuits by interacting with an engineer in natural language (like a chatGPT with specialist knowledge)? Similar comments could be made about systems that would take care of the most tedious bits of testing and verifying increasingly complex systems such as mobile phone chipsets or aircraft avionics software, or indeed for modelling and simulating electronics (both potentially achievable by using semi-automated AI coders such as

Google's "PaLM" model). This is precisely the cocktail of technologies that APRIL seeks to develop.

In this future, AI - with its capabilities of finding relevant information, performing simple tasks when instructed to do so and its incredible speed - would operate under the supervision of experienced engineers for assisting them in creating electronics suited to an ever-increasing palette of requirements, from low-power systems to chips manufactured to be recyclable to ultra-secure systems for handling the most sensitive and private data. To achieve this, APRIL brings together a large consortium of universities, industry and government bodies, working together to develop: i) the new technologies of the future, ii) the tools that will make these technologies a reality and very importantly, iii) the people with the necessary skills (for building as well as using such new tools) to ensure that the UK remains a capable and technologically advanced player in the global electronics industry.

Key Findings
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Potential use in non-academic contexts
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