Abstract

Multi-die FPGAs are widely adopted for large-scale accelerators, but optimizing high-level synthesis designs on these FPGAs faces two challenges. First, the delay caused by die-crossing nets creates an NP-hard floorplanning problem. Second, traditional directive optimization cannot consider resource constraints on each die or the timing issue incurred by the die-crossings. Furthermore, the high algorithmic complexity and the large scale lead to extended runtime for legalizing the floorplan of HLS designs under different directive configurations. To co-optimize the directives and floorplan of HLS designs on multi-die FPGAs, we formulate the co-search based on bin-packing variants and present two iterative optimization flows. The first (FADO 1.0) relies on a pre-built QoR library. It involves a greedy, latency-bottleneck-guided directive search, and an incremental floorplan legalization. Compared with a global floorplanning solution, it takes 693X~4925X shorter search time and achieves 1.16X~8.78X better design performance, measured in workload execution time. To remove the time-consuming QoR library generation, the second flow (FADO 2.0) integrates an analytical QoR model and redesigns the directive search to accelerate convergence. Through experiments on mixed dataflow and non-dataflow designs, compared with 1.0, FADO 2.0 further yields a 1.40X better design performance on average after implementation on the Alveo U250 FPGA.

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