Project overview
The Centre for Spatial Computational Learning is an international collaborative research centre, bringing together experts from Imperial College, the University of Southampton, the University of Toronto, and the University of California Los Angeles.
The rise of the Deep Neural Network as an increasingly universal paradigm of computation has been the defining feature across much of computing in the last few years. At the same time, the traditional von Neumann processor paradigm is being challenged by the rise of hardware spatial computational accelerators, such as FPGAs, which face serious usability and programmability challenges. The deep learning application domain is narrow enough to allow us to reconsider the entire stack of spatial compute, from arithmetic circuits to cloud-based systems, in an integrated and domain-specific manner. We have strong expertise across the breadth of this space. Our joint research centre has the potential to put the UK's expertise at the centre of the technology revolutionising the way high performance and low energy computation is specified and delivered, opening opportunities from ultra-low energy machine learning in internet of things devices through to powering scientific discovery in high-end server farms.
This centre marks a break-through in international coordination of research in the field. We: (a) work together to evolve a joint research strategy, (b) deliver elements of that joint research strategy through the staff employed on this grant as well as through academic staff and PhD students in all institutions, (c) facilitate a managed researcher exchange programme through funding exchange / secondment expenses for investigators and visiting researchers, as well as supporting secondments to/from industry, (d) hold annual showcase events of our work, and (e) deliver high-quality R&D and STEM advocacy outreach.
By the end of the project, we expect to have a self-sustaining momentum of internationally-coordinated research, incorporating the initial investigators, but extending beyond to new groups and the network of SMEs developing in this area.
The rise of the Deep Neural Network as an increasingly universal paradigm of computation has been the defining feature across much of computing in the last few years. At the same time, the traditional von Neumann processor paradigm is being challenged by the rise of hardware spatial computational accelerators, such as FPGAs, which face serious usability and programmability challenges. The deep learning application domain is narrow enough to allow us to reconsider the entire stack of spatial compute, from arithmetic circuits to cloud-based systems, in an integrated and domain-specific manner. We have strong expertise across the breadth of this space. Our joint research centre has the potential to put the UK's expertise at the centre of the technology revolutionising the way high performance and low energy computation is specified and delivered, opening opportunities from ultra-low energy machine learning in internet of things devices through to powering scientific discovery in high-end server farms.
This centre marks a break-through in international coordination of research in the field. We: (a) work together to evolve a joint research strategy, (b) deliver elements of that joint research strategy through the staff employed on this grant as well as through academic staff and PhD students in all institutions, (c) facilitate a managed researcher exchange programme through funding exchange / secondment expenses for investigators and visiting researchers, as well as supporting secondments to/from industry, (d) hold annual showcase events of our work, and (e) deliver high-quality R&D and STEM advocacy outreach.
By the end of the project, we expect to have a self-sustaining momentum of internationally-coordinated research, incorporating the initial investigators, but extending beyond to new groups and the network of SMEs developing in this area.
Staff
Lead researchers
Other researchers
Collaborating research institutes, centres and groups
Research outputs
Shannon How Shi Qi, Jagmohan Chauhan, Geoff V. Merrett & Jonathan Hare,
2023
Type: conference
Anastasios Dimitriou, Mingyu Hu, Jonathon Hare & Geoff Merrett,
2023
Type: conference
Lei Xun, Bashir Al-Hashimi, Jonathon Hare & Geoff Merrett,
2022
Type: conference
Sulaiman Sadiq, Jonathon Hare, Partha Maji, Simon Craske & Geoff Merrett,
2022
Type: conference
Mohammadamin Sabetsarvestani, Jonathon Hare, Bashir Al-Hashimi & Geoff Merrett,
2021, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 41(11)
Type: article