Identifying and placing heterogeneously-sized cluster groupings based on FPGA placement data

Field Programmable Gate Arrays (FPGAs) CAD flow run-time has increased due to the rapid growth in size of designs and FPGAs. Researchers are trying to find new ways to improve compilation time without degrading design performance. In this paper, we present a novel approach that identifies tightly grouped FPGA logic blocks and then uses this information during circuit placement. Our approach is an orthogonal optimization applicable in incremental design and physical optimization, and reduces placement run-time. Specifically, we present a new algorithm that analyzes designs post-placement to extract medium-grained super-clusters that consist of two to seventeen clusters, which we call “gems”. We modified VPR's simulated annealing placement algorithm to place our mixture of gems and clusters. Our new “Singularity Annealing” algorithm first crushes each cluster grouping into a “singularity” (treated as a single cluster). Then, the Singularity Annealer is run over this condensed circuit to obtain an initial placement, followed by an expansion of the singularities. Finally, we run a second low-temperature annealing phase on the entire expanded circuit. Our results show that our system reduces placement run-time on average by 17% while maintains the designs critical path delay, and increases designs channel width, and wirelength by 2% and 6.3%, respectively. We have also presented a test case to show the re-usability of gems in an incremental design example.