Heterogeneity in HPC nodes appears as a promising solution to improve the execution of a wide range of scientific applications, regarding both performance and energy consumption. Unlike CPUs and GPUs, FPGAs can be configured to fit the application needs, making them an appealing target to extend traditional heterogeneous HPC architectures. However, exploiting them requires an in-depth knowledge of low-level hardware and high expertise on vendor-provided tools, which should not be the primary concern of HPC application programmers. In this paper, we present the first results of the HEAVEN project that aims at designing a framework enabling a more straightforward development of scientific applications over FPGA enhanced platforms. Our work is concentrated on providing a framework, which will require the minimum knowledge of the underlying architecture, as well as fewer changes to the existing code. To fulfill these requirements, we extend the StarPU task programming library that initially targets heterogeneous architectures to support FPGA. We use Vivado HLS, a high-level synthesis tool to deliver efficient hardware implementations of the tasks from high-level languages like C/C++. For the evaluation of our proposal, we present code snippets for a blocking version of matrix multiplication, illustrating the ease of development our approach delivers. We also show preliminary results regarding the performance of the FPGA version, which validate our proof-of-concept implementation.