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Details of Grant 

EPSRC Reference: GR/R74567/01
Title: Silicon Spiking Systems: Collective Parallel Computation and Adaptive Sensory Systems
Principal Investigator: Murray, Professor AF
Other Investigators:
Reekie, Dr HM Renshaw, Dr D
Researcher Co-Investigators:
Project Partners:
Department: Sch of Engineering
Organisation: University of Edinburgh
Scheme: Standard Research (Pre-FEC)
Starts: 01 July 2002 Ends: 31 December 2005 Value (£): 339,907
EPSRC Research Topic Classifications:
Digital Signal Processing New & Emerging Comp. Paradigms
System on Chip VLSI Design
EPSRC Industrial Sector Classifications:
Electronics
Related Grants:
GR/R74581/01 GR/R74574/01
Panel History:
Panel DatePanel NameOutcome
08 Nov 2001 Electronics, Comms & Functional Materials Deferred
Summary on Grant Application Form
As System-on-Chip transistor density increases, new applications will emerge and new architectures must be explored for VLSI chips with more than 10^7 transistors. As feature size decreases to 500 Angstroms (towards the end of this decade), mismatch and noise problems impact in a manner and at a level far beyond that in current technology. For the most part, the design community has not begun to address this problem. While some ameliorating technological steps are emerging, most of the imperfections in such tiny devices can not be made to disappear and radically new paradigms are required to perform useful computation with small, inaccurate, noisy and (if low-power) slow devices. Neural systems have evolved that compute accurately and rapidly in the presence of similar problems. For example, neuronal sensor systems retrieve perceptually relevant information accurately and rapidly, through slow and noisy processing units and despite the intrinsic noise in the system. We propose solutions which are similar to those taken by neural systems: collective computation techniques, based on spiking neurons, techniques which make use of internal adaptivity (mediated by floating gates) to provide resilient parallel computing. We will implement CMOS chips to support and explore these techniques, applying them to tasks in real-time sensing, including auditory scene analysis and distance analysis in vision.
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