BackgroundMany of today’s interesting problems stem from the ability to generate and process large volumes of data, such as for instance, intelligent power grids and smart cities that form part of the Internet of Things. But the ability to work with all this data has to match the demand and as Moore’s law stops scaling, chipmakers will no longer be able to shrink transistors small enough to continue the trend of doubling how many they can fit on their integrated circuits every 12 or 18 months. Clearly, if the speed of processing power is to continue to develop to meet such demands, new forms of computing need to be found; new algorithms need to be developed to make efficient use of these new forms of computation; and new mathematical challenges arise in the design and analysis of these new algorithms.
In addition to the development of quantum computers, a number of novel computational paradigms, or next-generation computing architectures are emerging and more are likely to follow. Many have been inspired by the fundamental structure and function of the human brain. New computing paradigms are needed that are not only faster, use less power and are physically smaller, as well as those that could enable data storage/processing in contexts where current paradigms would be too expensive.
Neuromorphic computing for example, has included the development of chips that use “spiking neurons” as the basic computational building block. They attempt to model in silicon the massively parallel way the brain processes information as billions of neurons and trillions of synapses respond to sensory inputs such as visual and auditory stimuli. The implementation of neuromorphic computing at the hardware level can be realized by oxide-based memristors, threshold switches and transistors. Memristors are materials based on molecular or ionic mechanisms which act as conductors, emulating biological systems.
Similarly, massively-parallel computing structures, such as that developed as part of the UK’s Project Spinnaker, use “spiking networks” to sensibly simulate, in real time, the behaviour of a billion neurons. Additionally, advances in DNA-based data processing and storage are predicted to have a significant influence on theoretical and practical progress in the computer sciences.
So a key question is - what could you compute on new forms of computation?
Aims and ObjectivesThis workshop is a collaboration with GCHQ and aims to investigate potential next-generation advances in novel computational paradigms. A key aim is to bring together relevant stakeholders from across various UK research communities and industry. It is hoped that this activity will help to build closer links and collaborations and aid the establishment of a joined up multi-disciplinary UK community for this area. Disciplines identified so far as being relevant include synthetic biology, neuroscience, metamaterials, electronics/electrical engineering, AI/Machine learning, computer science and robotics and physics.
The event will also provide a forum for identifying challenges and increasing awareness of R&D activities across the different elements of the research communities. It is hoped that this will help to gain consensus on what the future research directions should be, for novel computational technologies, stimulating further interest from end-users towards helping to develop and invest in the novel computer paradigms area.Over the two days, this event will include presentations from researchers as well as an end-user session, where ‘problem holders’ will present on current and future challenges and reflect how new computational innovations might be of benefit and how they might be implemented. Areas covered will include a number of key current and future research directions will be highlighted including:
Neuromorphic computing - such as memristors and massively parallel computing structures
Biologically inspired paradigms – DNA based computation and storage
Materials for novel circuits
Novel architectures
Problem owner perspectives – to include security, healthcare and financial areas
