People can often see these days adverts on media company vans like "Grab your life by the Gigabits" (Virgin Media). Similar slogans appear on IT company flyers offering data analysis at Tera operations per second. They show the undeniable progress in technology, though still rarely we see performance growth per energy, for example Gigabits per Joule. And yet we are increasingly having to face with rising energy bills. As appetites for extending our intelligence wider and deeper into our everyday life steadily grow the grand challenge of ICT in making intelligence energy efficient becomes more and more evident. A significant role in this belongs to the research that aims at finding better methods for machine learning and data classification where both power and time for performing key operations in learning are reduced. In simple terms reducing power amounts to reducing average switching activity of electronic hardware, while reducing time means determining the moments when the learning actions have reached the state of sufficient quality. Self-time hardware, which works on the event-driven principles, in combination with novel machine learning methods, based on efficient approximation and Boolean logic as opposed to heavy arithmetic, gives this research a lever of innovation and potential impact against the state of the art. This project will investigate opportunities for improving performance and energy efficiency in artificial intelligence hardware created by the inherent time and power elasticity of self-timed circuits. The project will lay foundation to a new design methodology for building electronic devices and systems with machine learning (ML) capabilities at the micro- and nano-scale granularity. Those devices will be widely leveraged in many at-the-edge applications such as environmental sensors, traffic monitors, wearables, as well potential commodity ML-enhanced devices that can be used as building blocks in computer systems of the future. Micro- and nanoclassifiers and decision makers that can operate in real-time with power/energy efficiency are expected to find many 'light-weight' applications, so optimal (in terms of latency and energy) control is crucial. Here is an example of handwritten character recognition by an electronic pen with energy-harvested power. A reference class is given (e.g., digit "5"). Then, a few attempts in handwriting of digit 5 are made. During all these attempts training is performed. Then another reference class is given, and similar training is performed on it, and so on. The key requirements are to keep time spent limited and consumed energy minimised. Training is to be done to the best of the achievable accuracy. There are several trade-offs involved between speed and power and accuracy of learning. The success of the project will be measured in terms of the answers to the key research questions about the dynamics of machine learning in self-timed circuits; for example, whether the asynchronous design approach combined with the use of learning automata and logic-based inference will reach minimum energy point for a given machine learning problem. The project outcomes in theory and design methodology will be validated by means of extensive simulations, prototyping, IC fabrication and testing, and, ultimately, via an embodiment of the new hardware solutions into a concrete IoT application. A particularly challenging and breaking through validation will be the development and fabrication of the first asynchronous machine learning integrated circuit using flexible substrates. The practical impact of this research will be in the directions and methods of designing intelligent embedded electronics that will be capable of performing run-time classification of data obtained from environmental sensors, audio and image signals, as well as fast moving consumer goods (FMCG) and smart packaging using flexible IC technology.

SONNETS - Scalability Oriented Novel Networks of Event Triggered Systems - takes a clean-slate approach to next-generation computer modelling and artificial intelligence. To drive this we have an over-arching research goal that is both nationally important and challenging: real-time modelling of UK financial risk. It is easy to identify underlying risks after they cause a financial crisis. With hindsight, the 2008 financial crash was caused by too many banks buying too many risky mortgages. Whilst the crisis was unfolding it was all new information: no-one realised how many banks owned the risky mortgages. Then it was assumed that mortgage defaults were unlikely. Finally, it was assumed that losses in a few banks would not affect the national economy. The problem was a lack of visibility and understanding of the national picture: each bank appeared to have a manageable risk level, but most banks in the UK were exposed to the same underlying risk factor, so once mortgages started defaulting most banks started losing money and a perfect financial storm developed. What we needed then, and still do now, is national-level risk modelling that can consider risk across banks as it occurs. Modelling risk for one bank is a difficult problem, and modelling the entire UK is much harder. Banks have complex constantly changing portfolios, so building a picture of "who owns what" means tracking millions of trades per day. Even if we have that picture we still need to somehow assess risk, but that requires anticipating the future: we must pre-emptively identify potential scenarios, then estimate how much is lost in each scenario. Currently regulators use "stress tests" to identify national risk - they define a possible challenging economic scenario, then ask all the banks to estimate how much they might lose. However, this is both slow - the process takes months - and limited - they only explore one very severe scenario, which probably isn't the one that causes the problem. SONNETS will create a system that performs national-level risk analysis in real-time, by building a "digital twin" of the UK's financial system and using it to continually generate plausible future scenarios and assess their risk. We then use artificial intelligence to learn what risky scenarios look like. This gives regulators completely new tools: - A day-by-day view of the current national-risk of the UK, rather than waiting months for stress tests; - The ability to look forwards to identify and mitigate previously unknown risks as they develop, rather than waiting for a financial crisis to reveal them. We tackle this problem by addressing challenges in three main areas: - Computing: new paradigms for creating and running programs, exploiting multiple types of computer hardware distributed across the cloud; - Artificial Intelligence: methods for continual learning that can be split into multiple pieces, so that learning processes can be moved closer to the data they are learning from; - Modelling: theory and tools for automatic scenario generation, plus the ability to assess risk over large-scale models of the UK's financial institutions. These three areas are tightly linked, with the new computing paradigms supporting execution of the new AI and modelling in the cloud, and a synergistic relationship between the modelling of the system and learning about the model. Underpinning these three areas is the idea of event-triggered computing, where programs are split up into small fragments which send messages to each other. Using this event-triggered approach we can scale the risk analysis system up to support national-level risk analysis. It will constantly assess how risky the UK currently is, while trying to anticipate what scenarios might lead to financial crises in the future. SONNETS will provide a powerful tool to detect and mitigate financial risk as it is building up, rather than trying to react to a financial crisis once it happens.

The Northern Health Futures (NortHFutures) hub aims to create a world-leading healthcare technology (health-tech) development ecosystem. This will address unmet health needs and inequalities by supporting: inclusive digital skills training and sharing; research, innovation and entrepreneurship, enabled by digital design. Based in the North East and North Cumbria (NENC), with national and global reach, NortHFutures will support underserved communities, as it is known that national disparity of investment in NENC negatively impacts population health and wellbeing, and that a 'levelling up' of investment is needed to stimulate socio-economic and cultural growth for all, to encourage living and ageing well. NortHFutures builds upon the joined-up NENC approach to people-powered digital health innovation, as our regional Integrated Care Board (ICB) uniquely involves local authorities, communities, and citizens. Academic team members have a research track record that is stakeholder-involved and civic- and community-engaged. They are world-leading on understanding (i) health inequalities from medical, social, and design perspectives, and (ii) the opportunities for enrichment and enablement related to ageing well, connecting rural and urban populations, and pioneering applications of data science. In the pilot phase, we draw on this specialist expertise to address evidenced unmet health needs in NENC, (which have national and global importance): children and young people's health and nutrition; mental health and wellbeing; development of digital surgical pathways (for monitoring patient journeys beyond the hospital); living well with multiple long-term conditions. We combine the strengths and resources of 6 universities (Newcastle, Cumbria, Durham, Northumbria, Sunderland and Teesside), bringing regional investment in NIHR services, facilities and Applied Research Collaborations, plus National Innovation Centres for Ageing (NICA), Data (NICD) and Rural Enterprise (NICRE), National Horizons Centre (NHC), EPSRC Digital Economy programmes in data and digital citizens, and Health Data Research UK, the UK's national institute for health data science. NortHFutures supports new planned Centres, including Northumbria's Centre for Health & Social Equity and Cumbria's new campus and medical school. These University offers combine with an extensive partner network, including: ICB-NENC, 7 NHS Trusts, NHS Business Services Authority, Department of Health and Social Care, Health Education England; VCSE organisations delivering community-based services; industry partners - from SMEs to global tech giants; civic bodies such as Local and Combined Authorities; existing health research networks (e.g. AHSN-NENC, Newcastle Health Innovation Partnership); and innovation accelerators (e.g. Innovation SuperNetwork). Through an integrated, regional approach uniting this consortium for the first time, NortHFutures ambitiously aims to establish global leadership in Digital Health. To deliver this we will develop a supportive community infrastructure. We will co-design a digital brokerage service to connect and amplify partners' work, to offer and consume expertise, services and facilities (supporting acceleration of health-tech companies at differing tech-readiness levels). We will pioneer a Live Digital Health Databank, to explore, and train for, advanced healthcare data analytics, combining live data flows with care records (e.g. Great North Care Record). This will support personalised health diagnostics and interventions, giving our hub a unique value proposition to companies wishing to explore advanced data technologies. We will invest in Extended Reality pilots, to open up possibilities for clinical practice and service delivery. Our approaches will embed Responsible Research and Innovation (RRI), and Patient and Public Engagement (PPIE) throughout, to deliver health-tech that supports care beyond the hospital and is co-designed with end-users.