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High-level synthesis (HLS) enhances digital hardware design productivity through a high abstraction level. Even if the HLS abstraction prevents fine-grained manual register-transfer level (RTL) optimizations, it also enables automatable optimizations that would be unfeasible or hard to automate at RTL. Specifically, we propose a task-level multi-pumping methodology to reduce resource utilization, particularly digital signal processors (DSPs), while preserving the throughput of HLS kernels modeled as dataflow graphs (DFGs) targeting field-programmable gate arrays. The methodology exploits the HLS resource sharing to automatically insert the logic for reusing the same functional unit for different operations. In addition, it relies on multi-clock DFGs to run the multi-pumped tasks at higher frequencies. The methodology scales the pipeline initiation interval (II) and the clock frequency constraints of resource-intensive tasks by a multi-pumping factor (M). The looser II allows sharing the same resource among M different operations, while the tighter clock frequency preserves the throughput. We verified that our methodology opens a new Pareto front in the throughput and resource space by applying it to open-source HLS designs using state-of-the-art commercial HLS and implementation tools by Xilinx. The multi-pumped designs require up to 40% fewer DSP resources at the same throughput as the original designs optimized for performance (i.e., running at the maximum clock frequency) and achieve up to 50% better throughput using the same DSPs as the original designs optimized for resources with a single clock.

A DSP shared is a DSP earned: HLS Task-Level Multi-Pumping for High-Performance Low-Resource Designs