AI and hardware company Normal Computing UK was picked as one of 12 teams awarded funding from the Advanced Research + Invention Agency (ARIA) Scaling Compute Programme. This program, backed by £50 million in funding, aims to reduce AI hardware costs by 1000 times while diversifying the semiconductor supply chain.
Normal Computing’s hardware initiative, which was led by Chief Scientist Dr. Patrick Coles (formerly from Los Alamos National Laboratory) will bring expertise in noise-based computing and thermodynamics to develop physics-based computing chips for matrix inversion and explore applications in training large-scale AI models to transform AI hardware efficiency. And Normal Computing’s trademark thermodynamic computing approach utilizes noise as a resource rather than fighting against it, aligning with ARIA’s vision to challenge conventional computing paradigms – and to support breakthrough R&D for the UK and beyond.
Normal Computing’s ARIA R&D Creators have expertise from quantum computing and thermodynamics, probabilistic machine learning, and semiconductor design. And the key team members include Dr. Gavin Crooks (known for the Crooks fluctuation theorem in thermodynamics) and silicon engineering experts Zachary Belateche and Vincent Cheung – who recently exited their last chip startup Radical Semiconductor – and senior technical staff from Graphcore and Broadcom.
KEY QUOTES:
“We are unique in that AI is helping to design and manufacture our AI chips.”
– Faris Sbahi, CEO at Normal Computing
“The inefficiencies of digital hardware for AI are widely known – one ChatGPT session requires 150 times more power than all-encompassing brain processing. Through ARIA’s Scaling Compute programme, we’re pushing towards the fundamental limits of computational efficiency by allowing physical dynamics, like thermal equilibration, to do computations for us.”
– Dr. Patrick Coles, Chief Scientist at Normal Computing
“We are unique in that AI is helping to design and manufacture our AI chips. The industry struggles to tackle the ‘AI energy crisis’ because of the ‘silicon complexity crisis.’ Even for the simplest kinds of physical architectures, like memory, complexity is now at the PhD level, so to speak. We trained the first AI to genuinely understand formal chip logic in order to help de-risk chips for our several commercial partners and now with ARIA. It’s analogous to DeepMind’s AlphaGeometry, but for hardware instead of mathematics, and this work is led by former AI leads from Meta and Google Brain.”
– Faris Sbahi, CEO at Normal Computing
“If successful, this programme will unlock a new technological lever for next-generation AI hardware, alleviate dependence on leading-edge chip manufacturing, and open up new avenues to scale AI hardware.”
– Suraj Bramhavar, ARIA’s Programme Director for Scaling Compute
