Nature Computes Better
We can redefine the way computers process information by exploiting principles found ubiquitously in nature. By better understanding how the natural world around us performs computation, we'll build dramatically more efficient computers.
The core beliefs that underpin this opportunity space:
The growth of AI exacerbates an already unsustainable demand for compute → we need alternative scaling pathways.
Natural systems are orders of magnitude more efficient than silicon microprocessors at a wide range of computational tasks → a stronger understanding of how living systems compute is needed to advance both engineering biology and the creation of new hardware.
Investigating the role of statistical physics and nonlinear dynamics in novel hardware represents a significantly underexplored opportunity → exploiting these approaches is likely to yield new modalities for AI processing.
Modern AI has massive and broad applicability but is underpinned by a narrow set of mathematical kernels → this presents a unique opportunity for the development of next-generation computing paradigms.
Observations
Some signposts as to why we see this area as important, underserved, and ripe.
Programme spotlight: Scaling Compute
The history of computing hardware has been defined by scientists and engineers realising that radical new approaches are required to meet our technological needs.
From strained silicon to EUV lithography, from FinFETs to silicon photonics – each breakthrough, once deemed impractical or unattainable, became a cornerstone of our technological age. Yet the insatiable demand for more computing power made these technologies necessary and eventually foundational.
Backed by £42m, Scaling Compute unites expertise across three critical technology domains (AI systems design, mixed-signal CMOS circuits, and advanced networking) to continue this tradition by looking to redefine our current compute paradigm.
Meet the programme team
Suraj Bramhavar is an electrical engineer. His work focuses on how we can redefine the way computers process information to build dramatically more efficient computers. Suraj joined ARIA from Sync Computing, where he was co-founder and CTO, which optimises the use of modern cloud computing resources. The company was spun-out from his research at MIT Lincoln Laboratory. Suraj previously worked at Intel Corp, helping transition silicon photonics technology from an R&D effort into a business now worth over $1BN.
"Increasingly, the way we process information in the digital world is colliding with the way our natural world functions. This interdisciplinary space provides a unique opportunity to both uncover insights for improving the performance of modern computers, and also to provide an entirely new lens by which to understand/engineer nature."
Meet the programme team
Paolo is an electronic engineer by training, and has spent majority of his professional career in technical R&D roles for large high-tech companies, such as HP and Alcatel-Lucent. He returned to academia to earn a PhD in Machine Learning, then joined Graphcore, a startup that develops innovative AI hardware.
Meet the programme team
David trained as a chemist at University College London before transitioning to work in materials science at Imperial College London, where he developed nanocarbon devices for sensing, photovoltaics, and energy storage. Prior to joining ARIA, David built sales and operations functions at early stage startups, focusing on physics-based software for the automotive and consumer electronics industries. David supports ARIA as an Operating Partner from Pace.
Our other opportunity spaces
Our opportunity spaces are designed as an open invitation for researchers from across disciplines and institutions to learn with us and contribute – a variety of perspectives are just what we need to change what’s possible.