Emerging Technology, Deep Tech

Aston University and Hartree Centre Launch UK’s NeuroSYNC Roadmap

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May 16, 2026 by Ivy Delaney

Aston University and the Science and Technology Facilities Council’s Hartree Centre are establishing the Aston-Hartree Neuromorphic Centre of Competence, a new hub designed to accelerate the adoption of brain-inspired computing technologies across the United Kingdom. This partnership will connect academic researchers, industry leaders, and public sector organizations to advance neuromorphic computing, a non-digital and energy-efficient approach to processing information that differs from traditional methods. By mimicking the human brain, this technology promises smarter artificial intelligence and faster real-time decision-making for an increasingly data-driven world. “This collaboration brings together complementary strengths in advanced computing, emerging computing paradigms, and research and innovation,” said Professor Vassil Alexandrov, chief science officer at STFC Hartree Centre. The centre will focus on bridging the gap between research and practical application, with a particular emphasis on supporting small and medium-sized enterprises in the West Midlands and throughout the UK.

Aston-Hartree Partnership Accelerates Neuromorphic Computing Deployment

Neuromorphic computing differs from conventional digital methods by processing information in a non-digital and energy-efficient way, a crucial distinction as current computing architectures struggle with the escalating power demands of increasingly complex AI tasks. The Hartree Centre, a component of UK Research and Innovation (UKRI), will leverage its expertise in technology translation and industrial engagement to accelerate this adoption, serving as a key delivery partner for the Aston Institute of Photonic Technologies’ (AIPT) NeuroSYNC neuromorphic computing roadmap. This collaboration will focus on co-designing proof-of-concept applications and developing scalable solutions aligned with industry needs.

The collaboration formalized the establishment of the Aston-Hartree Neuromorphic Centre of Competence at Aston University, designed to serve as a central hub connecting academic research with industrial application of neuromorphic technologies. A key focus will be co-designing proof-of-concept applications and scalable solutions, particularly for small and medium-sized enterprises (SMEs) within the West Midlands and across the UK. “By working together, we can help accelerate the development of neuromorphic technologies and support their adoption in ways that deliver real impact for UK science and industry.” The partnership also aims to influence future procurement of neuromorphic systems, establishing benchmarks and integrating the technology into existing computing infrastructure. This newly formed center will connect academic researchers, industrial partners, and public sector organizations to foster collaborative innovation in neuromorphic systems, extending the reach of neuromorphic computing beyond academic labs with a new partnership designed to accelerate its practical application across multiple sectors.

Technology is changing the world. This joint statement of endeavour reflects a shared ambition to position the UK, Aston University, and West Midland – the historical heart of the Industrial Revolution, at the forefront of next-generation computing. Through blending scientific excellence and focus on real-world impact, this partnership will unlock the new transformative opportunities for the region.

Professor Sergei Turitsyn, director of AIPT, director of NeuroSYNC, Aston University
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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