Evolving component library for approximate high level synthesis
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- @InProceedings{Vaverka:2016:SSCI,
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author = "Filip Vaverka and Radek Hrbacek and Lukas Sekanina",
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booktitle = "2016 IEEE Symposium Series on Computational
Intelligence (SSCI)",
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title = "Evolving component library for approximate high level
synthesis",
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year = "2016",
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address = "Athens",
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month = "6-9 " # dec,
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming",
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DOI = "doi:10.1109/SSCI.2016.7850168",
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abstract = "An approximate computing approach has recently been
introduced for high level circuit synthesis (HLS) in
order to make good use of approximate circuits at
system and block level. It is assumed in HLS algorithms
that a component library containing various
implementations of elementary circuit components is
available. An open problem is how to construct such a
component library in the context of approximate
computing, where the component's error is a new design
variable and hence many compromise implementations
exist for a given component. In this paper, we first
introduce a multi-objective Cartesian genetic
programming method to create a comprehensive component
library containing hundreds of Pareto optimal
implementations of approximate 8-bit adders and
multipliers, where the error, area and delay are
simultaneously optimised. Another multi-objective
evolutionary algorithm is employed to solve the so
called binding problem of HLS, in which suitable
approximate components are assigned to nodes of the
data flow graph describing a complex digital circuit.
Two approaches are then proposed and compared in order
to reduce the size of the library of approximate
components. It is shown that a random sub-sampling of
the component library provides satisfactory results in
the context of our study. The proposed methods are
evaluated using two benchmark circuits - the reduce
(sum) and DCT circuits.",
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notes = "Also known as \cite{7850168}",
- }
Genetic Programming entries for
Filip Vaverka
Radek Hrbacek
Lukas Sekanina
Citations