High performance computing systems make increasing use of hardware accelerators to improve performance and power properties. For large high-performance FPGAs to be successfully integrated in such computing systems, methods to raise the abstraction level of FPGA programming are required. In this paper we propose a tool flow, which automatically generates highly optimized hardware multicore systems based on parameters. Profiling feedback is used to adjust these parameters to improve performance and lower the power consumption. For an image processing application we show that our tools are able to identify optimal performance energy trade-offs points for a multicore based FPGA accelerator.