One size does not fit all: Implementation trade-offs for iterative stencil computations on FPGAs

Iterative stencils are kernels in various application domains such as numerical simulations and medical imaging, that merit FPGA acceleration. The best architecture depends on many factors such as the target platform, off-chip memory bandwidth, problem size, and performance requirements. We generate a family of FPGA stencil accelerators targeting emerging System on Chip platforms, (e.g., Xilinx Zynq or Intel SoC). Our designs come with design knobs to explore trade-offs. We also propose performance models to hone in on the most interesting design points, and show how they accurately lead to optimal designs. The optimal choice depends on problem sizes and performance goals.