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2007
Springer

A Quantitative Prediction Model for Hardware/Software Partitioning

12 years 7 months ago
A Quantitative Prediction Model for Hardware/Software Partitioning
An important step in Heterogeneous System Development is Hardware/Software Partitioning. This process involves exploring a huge design space. By using profiling to select hot-spots and estimate area and delay we can prune the design space considerably. We present a Quantitative Model that makes early predictions to prune the design space and support the partitioning process. The model is based on Software Complexity Metrics, which capture important aspects of functions as control intensity, data intensity, and code size. To remedy interdependence among software metrics, we performed a Principal Component Analysis. The hardware characteristics were determined by automatically generating VHDL from C using the DWARV C-to-VHDL compiler. Linear regression on these data generated our model. The model error differs per hardware characteristic. We show that for flip-flops the mean error is 69%. In conclusion, our quantitative model makes fast and sufficiently accurate area predictions in ...
Roel Meeuws, Yana Yankova, Koen Bertels, Georgi Ga
Added 07 Jun 2010
Updated 07 Jun 2010
Type Conference
Year 2007
Where FPL
Authors Roel Meeuws, Yana Yankova, Koen Bertels, Georgi Gaydadjiev, Stamatis Vassiliadis
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