HIMap

Abstract

Recently, many models show their superiority in sequence and parameter tuning. However, they usually generate non-deterministic flows and require lots of training data. We thus propose a heuristic and iterative flow, namely HIMap, for deterministic logic synthesis. In which, domain knowledge of the functionality and parameters of synthesis operators and their correlations to netlist PPA is fully utilized to design synthesis templates for various objetives. We also introduce deterministic and effective heuristics to tune the templates with relatively fixed operator combinations and iteratively improve netlist PPA. Two nested iterations with local searching and early stopping can thus generate dynamic sequence for various circuits and reduce runtime. HIMap improves 13 best results of the EPFL combinational benchmarks for delay (5 for area). Especially, for several arithmetic benchmarks, HIMap significantly reduces LUT-6 levels by 11.6 ~ 21.2% and delay after P&R by 5.0 ~ 12.9%.