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Automated optimization of reconfigurable designs

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Kurek-M-2016-PhD-Thesis.pdfThesis9.91 MBAdobe PDFView/Open
Title: Automated optimization of reconfigurable designs
Authors: Kurek, Maciej
Item Type: Thesis or dissertation
Abstract: Currently, the optimization of reconfigurable design parameters is typically done manually and often involves substantial amount effort. The main focus of this thesis is to reduce this effort. The designer can focus on the implementation and design correctness, leaving the tools to carry out optimization. To address this, this thesis makes three main contributions. First, we present initial investigation of reconfigurable design optimization with the Machine Learning Optimizer (MLO) algorithm. The algorithm is based on surrogate model technology and particle swarm optimization. By using surrogate models the long hardware generation time is mitigated and automatic optimization is possible. For the first time, to the best of our knowledge, we show how those models can both predict when hardware generation will fail and how well will the design perform. Second, we introduce a new algorithm called Automatic Reconfigurable Design Efficient Global Optimization (ARDEGO), which is based on the Efficient Global Optimization (EGO) algorithm. Compared to MLO, it supports parallelism and uses a simpler optimization loop. As the ARDEGO algorithm uses multiple optimization compute nodes, its optimization speed is greatly improved relative to MLO. Hardware generation time is random in nature, two similar configurations can take vastly different amount of time to generate making parallelization complicated. The novelty is efficient use of the optimization compute nodes achieved through extension of the asynchronous parallel EGO algorithm to constrained problems. Third, we show how results of design synthesis and benchmarking can be reused when a design is ported to a different platform or when its code is revised. This is achieved through the new Auto-Transfer algorithm. A methodology to make the best use of available synthesis and benchmarking results is a novel contribution to design automation of reconfigurable systems.
Content Version: Open Access
Issue Date: Sep-2015
Date Awarded: Mar-2016
URI: http://hdl.handle.net/10044/1/39915
DOI: https://doi.org/10.25560/39915
Supervisor: Luk, Wayne
Sponsor/Funder: Engineering and Physical Sciences Research Council
Maxeler Technologies
Xilinx (Firm)
European Union
Funder's Grant Number: 248976
257906
287804
318521
Department: Computing
Publisher: Imperial College London
Qualification Level: Doctoral
Qualification Name: Doctor of Philosophy (PhD)
Appears in Collections:Computing PhD theses



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