Zero-change netlist transformations: A new technique for placement benchmarking
In this paper, the authors introduce the concept of zero-change netlist transformations (ZCNTs) to: 1) quantify the suboptimality of existing placers on artificially constructed instances and 2) "Partially" quantify the suboptimality of placers on synthesized netlists from arbitrary netlists by giving lower bounds to the suboptimality gap. Given a netlist and its placement from a placer, a class of netlist transformations that synthesizes a different netlist from the given netlist is formally defined, but yet the new netlist has the same half-perimeter wire length (HPWL) on the given placement. Furthermore, and more importantly, the optimal HPWL value of the new netlist is no less than that of the original netlist. By applying the transformations and reexecuting the placer, any deviation in RPWL as a lower bound to the gap from the optimal HPWL value of the new synthesized netlist can be interpreted. The transformations allow us to: 1) increase the cardinality of hyperedges; 2) reduce the number of hyperedges; and 3) increase the number of two-pin edges, while maintaining the placement HPWL constant. It is developed here methods that apply ZCNTs to synthesize netlists having typical netlist statistics. Furthermore, an. approach to estimate the suboptimality of other metrics, such as rectilinear minimum-spanning tree (RMST) and minimum-Steiner tree, is extended. Using these transformations, the suboptimatity of some of the existing academic placers (FengShui, Capo, mPL, Dragon) is studied on synthesized netlists from the IBM benchmarks with instances ranging from 10k to 210k placeable instances. The results show that current placers exhibit suboptimal behavior to ZCNTs with varying degree according to the placer. Systematic suboptimality deviations in HPWL and RMST are displayed on the synthesized netlists from IBM (version 1) benchmarks. The specific nature of the transformations points out troublesome netlist structures and possible directions for improvement in the existing placers.
placement benchmarking suboptimality algorithms performance wirelength