TY - GEN
T1 - A quantitative prediction model for hardware/software partitioning
AU - Meeuws, Roel
AU - Yankova, Yana
AU - Bertels, Koen
AU - Gaydadjiev, Georgi
AU - Vassiliadis, Stamatis
PY - 2007
Y1 - 2007
N2 - 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 support of early Hardware/Software Partitioning.
AB - 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 support of early Hardware/Software Partitioning.
UR - http://www.scopus.com/inward/record.url?scp=48149099074&partnerID=8YFLogxK
U2 - 10.1109/FPL.2007.4380757
DO - 10.1109/FPL.2007.4380757
M3 - Conference contribution
AN - SCOPUS:48149099074
SN - 1424410606
SN - 9781424410606
T3 - Proceedings - 2007 International Conference on Field Programmable Logic and Applications, FPL
SP - 735
EP - 739
BT - Proceedings - 2007 International Conference on Field Programmable Logic and Applications, FPL
T2 - 2007 International Conference on Field Programmable Logic and Applications, FPL
Y2 - 27 August 2007 through 29 August 2007
ER -