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Biologically-Inspired Massively-Parallel Computation (BIMPC)
Date du début: 1 mars 2013, Date de fin: 28 févr. 2018 PROJET  TERMINÉ 

"We aim to establish a world-leading research capability in Europe for advancing novel models of asynchronous computation based upon principles inspired by brain function. This work will accelerate progress towards an understanding of how the potential of brain-inspired many-core architectures may be harnessed. The results will include new brain-inspired models of asynchronous computation and new brain- inspired approaches to fault-tolerance and reliability in complex computer systems.Many-core processors are now established as the way forward for computing from embedded systems to supercomputers. An emerging problem with leading-edge silicon technology is a reduction in the yield and reliability of modern processors due to high variability in the manufacture of the components and interconnect as transistor geometries shrink towards atomic scales. We are faced with the longstanding problem of how to make use of a potentially large array of parallel processors, but with the new constraint that the individual elements are the system are inherently unreliable.The human brain remains as one of the great frontiers of science – how does this organ upon which we all depend so critically actually do its job? A great deal is known about the underlying technology – the neuron – and we can observe large-scale brain activity through techniques such as magnetic resonance imaging, but this knowledge barely starts to tell us how the brain works. Something is happening at the intermediate levels of processing that we have yet to begin to understand, but the essence of the brain's massively-parallel information processing capabilities and robustness to component failure lies in these intermediate levels.These two issues draws us towards two high-level research questions:• Can our growing understanding of brain function point the way to more efficient parallel, fault-tolerant computing?• Can massively parallel computing resources accelerate our understanding of brain function"