A methodology for identifying and placing heterogeneous cluster groups based on placement proximity data (abstract only)

Abstract

Due to the rapid growth in the size of designs and Field Programmable Gate Arrays (FPGAs), CAD run-time has increased dramatically. Reducing FPGA design compilation times without degrading circuit performance is crucial. In this work, we describe a novel approach for incremental design flows that both identifies tightly grouped FPGA logic blocks and then uses this information during circuit placement. Our approach reduces placement run-time on average by more than 17% while typically maintaining the design's critical path delay and marginally increasing its minimum channel width and wire length on average. Instead of following the traditional approach of evaluating a circuit's pre-placement netlist, this new algorithm analyzes designs post-placement to detect proximity data. It uses this information to non-aggressively extract heterogeneous cluster groupings from the design, which we call "gems," that consist of two to seventeen clusters. We modified VPR's simulated annealing placement algorithm to use our Singularity Placer, which first crushes each cluster grouping into a "singularity," to be treated as a single cluster. We then run the annealer over this condensed circuit, followed by an expansion of the singularities, and a second annealing phase for the entire expanded circuit.