Incorporating Physical Information into Clustering for FPGAs

Abstract

The traditional approach to FPGA clustering and CLB-level placement has been shown to yield significantly worse overall placement quality than approaches which allow BLEs to move during placement. In practice, however, modern FPGA architectures require computationally-expensive Design Rule Checks (DRC) which render BLE-level placement impractical.

This thesis research addresses this problem by proposing a novel clustering framework that produces better initial clusters that help to reduce the dependence on BLE-level placement. The work described in this dissertation includes: (1) a comparison of various clustering algorithms used for FPGAs, (2) the introduction of a novel hybridized clustering framework for timing-driven FPGA clustering, (3) the addition of physical information to make better clusters, (4) a comparison of the implemented approaches to known clustering tools, and (5) the implementation and evaluation of cluster improvement heuristics. The proposed techniques are quantified across accepted benchmarks and show that the implemented DPack produces results with 16% less wire length, 19% smaller minimum channel widths, and 8% less critical delay, on average, than known academic tools. The hybridized approach, HDPack, is found to achieve 21% less wire length, 24% smaller minimum channel widths, and 6% less critical delay, on average.