Generating ultrafast laser pulses at the nanoscale can provide a key component for optical interconnection in multi-core computer processors. It can significantly reduce the energy consumption for converting electrical signals into optical forms at high bandwidth, which is essential for future applications in the Internet of Things and Green Photonics. However, experimental approaches to generate ultrafast laser pulses at the nanoscale are yet to be explored. Based on Dr Jin's expertise in ultrafast photonics, he will develop a novel technology to generate laser pulses through the dynamic control of Purcell factor in coupled photonic crystal cavities. This technology is not fundamentally limited by the spontaneous emission lifetime and can thus achieve high speed modulation beyond 100GHz, ten times faster than conventional technologies. Dr Jin's research and industry track record, combined with the excellent laboratory environment at the University of Sheffield, will support the success of this timely and important research topic.

This proposal brings together a group of leading multidisciplinary teams in medical, chemical, metabolic, statistical and computational sciences from across Imperial College London (ICL, lead institution) and its partners. These include the Institute of Cancer Research, the European Molecular Biology Laboratory-European Bioinformatics Institute, the Universities of Oxford, Swansea and Nottingham, and the MRC Clinical Sciences Centre and MRC Human Nutrition Research Centre, supported by strong partnerships and collaborative links with industry, the NHS and the National Institute for Health Research funded Biomedical Research Centres and Units. The proposal seeks support to build a DEDICATED INFRASTRUCTURE in data storage, aggregation, analysis and visualisation of diverse types of biomedical data. These come from standard clinical sources through to different types of information including from genetic analysis, and metabolic information, that help us to define patients or people studied in the general population using a holistic "systems medicine" framework. The GLOBAL AIM is to make major advances in understanding the causes and reasons for disease progression of common human diseases such as cancer, cardiovascular disease, respiratory disease and metabolic disorders such as type 2 diabetes and obesity. In this way we aim to create new disease diagnostics and prognostics aimed at the individual patient ("stratified medicine") through innovations in medical bioinformatics with powerful computing capability. The programme will create unprecedented capacity to i) DEVELOP powerful new approaches for computation and analysis of large-scale, complex, multi-source medical data; ii) INTEGRATE information linking multiple different types of biological information from analysis of blood and urine samples (e.g., metabolic, genomic, analysis of the microbial genome) to different diseases, disease progression and outcomes; hence to iii) help UNDERSTAND the causes and mechanisms of disease and improve individual disease classification for better patient treatments and safety; also to iv) TRANSFORM the training of the biomedical researchers of the future through creation of a seamless interdisciplinary environment spanning biomedicine, physical sciences, computing and engineering. In particular we will capitalise on computational expertise that has led to the development of a partnership in medical information infrastructure and service between universities and the pharmaceutical industry called eTRIKS, and related software platforms such as tranSMART, an "open source" solution for managing data and research knowledge in clinical studies. We also have world-leading expertise in metabolic "fingerprinting" and systems medicine approaches manifested in the MRC-NIHR National Phenome Centre located at ICL, and underpinned by excellence in genomics, computational sciences and advanced data modelling and visualisation. This project has a very broad collaboration with industrial sectors including major pharmaceutical companies, instrument vendors, IT and informatics companies. Our project covers the complete healthcare envelope of data generating activity and analysis from the level of basic measurement sciences through to the understanding of gene-environment interactions and disease mechanisms to the creation of knowledge systems for better clinical decision making based on detailed knowledge of individual patient biology. This application is strengthened by the decision of ICL to establish a major interdisciplinary centre for 'big data' at the new Imperial West campus, ensuring sustainability over the longer term. The project aims to deliver top class science, a robust informatics platform and in-depth scientific data and knowledge to contribute to the state of the art of UK and international medical research.

About 55 years ago, Gordon Moore speculated that transistors will become smaller and more energy efficient every year. Since then, we have enjoyed exponentially increasing computer performance owing to what has been called the Moore's law. However, Moore's law is coming to an end and has already begun to disrupt the semiconductor industry. Absent the exponential performance and energy gains due to device scaling, industry has pivoted to hardware specialisation: targeting hardware to a specific computation class generally leads to orders of magnitude improvement in energy and performance. We are well and truly in the age of heterogeneous computing. A modern smartphone today has dozens of devices within a single chip, including CPUs, GPUs, and other accelerators. But efficiency hinges on reducing data movement between these devices; otherwise, it can seriously jeopardise the benefits of heterogeneous computing. Sadly, an analysis of Google workloads on a mobile device reveals that, on average, more than 60% of the overall energy is spent on moving around data. One promising approach to reducing data movement is called cache coherence. The cache coherence protocol, which automatically replicates data consistently, enables data to be accessed locally when it is safe to do so. Thus, it not only minimises data movement but it also does so in a programmer-transparent fashion. However, cache coherence protocols are notoriously hard to design and verify even for homogeneous multicores, where they have been deployed today. To make matters worse, we do not know how to keep the devices of a heterogeneous computer coherent correctly, in part because we do not yet understand what it means to be correct. In this project, we propose an entirely new way of designing coherence protocols. Instead of manually designing them and verifying them later, we propose an automatic method to generate them correctly. Our method is based on a new foundation of heterogeneous coherence called compound consistency models, which formally answers the question of how distinct coherence protocols should compose. If successful, the project will not only lift the major roadblock to efficient heterogeneous computing (data movements costs), it will also catalyse the burgeoning open hardware movement by democratising one of its trickiest components: cache coherence protocols.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.