Quantitative Hardware Prediction Modeling For Hardware/software Co-design
Abstract Category: Engineering
Course / Degree: Ph.D. in Computer Engineering
Institution / University: Delft University of Technology, Netherlands
Published in: 2012
Dissertation Abstract / Summary:
Hardware estimation is an important factor in Hardware/Software Co-design. In this dissertation, we present the Quipu Modeling Approach, a high-level quantitative prediction model for HW/SW Partitioning using statistical methods. Our approach uses linear regression between software complexity metrics and hardware characteristics. The resulting prediction models provide essential information for such Co-design tasks, as identifying resource intensive parts of the application, helping to evaluate different mapping options, and guiding code modifications.
We show that prediction models can be generated for different High Level Synthesis tools, reconfigurable devices, hardware measures, and application domains. To this purpose, we present a detailed investigation of several Quipu prediction models targeting each of these different dimensions. In addition, an extensive description is given of the targeting of the Quipu Modeling Approach to a new tool and platform within a few days. We evaluate the quality of our models by carefully investigating the error behavior, which ranges from 2.4%, for a domain-specific model targeting slices, to 39.7%, for a domain-agnostic model targeting the number of controller states.
As a demonstration of the practical use of Quipu Prediction models, we present a case study of two applications. These applications were analyzed and partitioned for the Molen Machine Organization. We show how Quipu prediction models play an important role in evaluating area constraints and performing Design Space Exploration. The two applications had an increased performance of 192% and 30%.
Dissertation Keywords/Search Tags:
reconfigurable computing,statistical modeling,hardware estimation,high level synthesis
This Dissertation Abstract may be cited as follows:
R. J. Meeuws, Quantitative Hardware Prediction Modeling for Hardware/Software Co-design, pp. 187, Delft, The Netherlands, July 2012, ISBN 978-94-6186-038-5, PhD Thesis
Submission Details: Dissertation Abstract submitted by Roel Meeuws from Netherlands on 11-Jul-2012 09:56.
Abstract has been viewed 13881 times (since 7 Mar 2010).
Roel Meeuws Contact Details: Email: r.j.meeuws@gmail.com
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