Delft University of Technology
In this paper, the authors discuss Quipu their multi-dimensional quantitative prediction model for hardware-software partitioning. The proposed model is based on linear regression between software metrics determined on a dataset of 127 kernels and measures from their corresponding hardware designs. These software metrics capture the complexity of the C language description. The hardware designs are determined using the DWARV C-to-VHDL translator. Currently, Quipu exhibits a relatively large error com-pared to lower level approaches, however the Quipu model can make fast and early predictions and is applicable to a wide variety of applications.