In the last decade, FPGAs appeared as a credible alternative for big data and high-performance computing applications. However, programming an FPGA is tedious: given a function to implement, the circuit must be designed from scratch by the developer. In this short paper, we address the compilation of data placement under parallelism and resource constraints. We propose an HLS algorithm able to partition the data across memory banks, so parallel accesses will target distinct banks to avoid data transfer serialization. Our algorithm is able to reduce the number of banks and the maximal bank size. Preliminary evaluation shows promising results.