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Many numerical simulation applications from the scientific, financial and machine-learning domains require large amounts of compute capacity. They can often be implemented with a streaming data-flow architecture. Field Programmable Gate Arrays (FPGA) are particularly power-efficient hardware architectures suitable for streaming data-flow applications. Although numerous programming languages and frameworks target FPGAs, expert knowledge is still required to optimise the throughput of such applications for each target FPGA device. The process of selecting which optimising transformations to apply, and where to apply them is dubbed Design Space Exploration (DSE). We contribute an elegant and efficient compiler based DSE strategy for FPGAs by merging information sourced from the compiled application's semantic structure, an accurate cost-performance model and a description of hardware resource limits for particular FPGAs. Our work leverages developments in functional programming and dependent type theory to bring performance portability to the realm of High-Level Synthesis (HLS) tools targeting FPGAs. We showcase our approach by presenting achievable speedups for three example applications. Results indicate considerable improvements in throughput of up to 58× in one example. These results are obtained by traversing a minute fraction of the total Design Space.

In this paper we present a novel approach to program optimisation based on compiler-based type-driven program transformations and a fast and accurate cost/performance model for the target architecture. We target streaming programs for the problem domain of scientific computing, such as numerical weather prediction. We present our theoretical framework for type-driven program transformation, our target high-level language and intermediate representation languages and the cost model and demonstrate the effectiveness of our approach by comparison with a commercial toolchain.