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Projects: Projects for Investigator
Reference Number EP/P010946/1
Title Coarse Approximator Compilation
Status Completed
Energy Categories Energy Efficiency(Residential and commercial) 10%;
Not Energy Related 90%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 100%
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Dr C Fensch
No email address given
Sch of Mathematical and Computer Science
Heriot-Watt University
Award Type Standard
Funding Source EPSRC
Start Date 01 April 2017
End Date 30 September 2018
Duration 18 months
Total Grant Value £99,816
Industrial Sectors Information Technologies
Region Scotland
Programme NC : ICT
Investigators Principal Investigator Dr C Fensch , Sch of Mathematical and Computer Science, Heriot-Watt University (100.000%)
  Industrial Collaborator Project Contact , University of Washington, USA (0.000%)
Project Contact , Codeplay Software Ltd (0.000%)
Web Site
Abstract Computers have revolutionised our lives, from mobile phones that exceed the computational power of early supercomputers by orders of magnitudes, to today's supercomputers that help discovery of new drugs to cure serious diseases and to design more energy efficient vehicles and buildings. All this progress has been made possible by continuously increasing computational power. However, there are two threats to this trend. First, harnessing this resource has become increasingly difficult. Imagine a car that provides direct control of fuel mix, 20 gears and adjustable valve timing. This car will provide excellent performance, but requires a driver with an engineering degree to make the optimal adjustments. Second, similar to improved car fuel efficiency, there is increasing demand for improved computational energy efficiency. We cannot attach larger batteries to a mobile phone, or build a nuclear power station next to each data centre. While there are ongoing discoveries that improve efficiency, these solutions intensify the first problem: they increase the difficulty. For a solution to be truly practical it needs to be usable by non-experts! This project aims to address this in case of Approximate Computing -- a recently proposed technology aiming to increase energy efficiency by orders of magnitude.The basic insight of approximate computing is that, traditionally, computers always provide a precise and exact solution instead of a good enough solution. This obsession with precision is very energy wasteful. Imagine that you quickly look into your wallet to check how much cash you carry, you wonder if it is a 1-2 GBP, about 20 GBP or more than 50 GBP. One usually does not really care if it is 17.42 GBP or 17.43 GBP. In such a situation, it would be a waste of time to count the cash precisely. Research has shown that a vast body of problems can take advantage of this kind of imprecision. This research project aims to make approximate computing technology available to non-expert programmers. In particular, the main obstacle for widespread adaptation is that current state of the art in approximate computing burdens the application programmer with providing a suitable approximate alternative. This is comparable to burdening the driver of a car with sophisticated mechanical tasks such as changing a timing belt. The premise of this project is that it is possible to derive an approximation automatically and that this process should be integrated with the tool that every programmer already uses -- the compiler
Publications (none)
Final Report (none)
Added to Database 07/08/17