Efficient Design Space Exploration for Dynamic & Speculative High-Level Synthesis

High-Level Synthesis performs well for compute-intensive loops with regular control but struggles to uncover parallelism in kernels with complex control-flow. Novel scheduling techniques based on dynamic scheduling and speculation have been proposed to address this issue. Although they outperform classical static scheduling techniques, they also come at a significant area overhead. Precisely determining where and by how much to apply these techniques remains an open problem, which we address in this work through an efficient exploration algorithm (combining pruning and search heuristics). We show that our approach can explore large solution spaces while producing efficient solutions.