Gate sizing using Lagrangian relaxation combined with a fast gradient-based pre-processing step

Abstract

In this paper, we present Forge, an optimal algorithm for gate sizing using the Elmore delay model. The algorithm utilizes Lagrangian relaxation with a fast gradient-based pre-processing step that provides an effective set of initial Lagrange multipliers. Compared to the previous Lagrangian-based approach, Forge is considerably faster and does not have the inefficiencies due to difficult-to-determine initial conditions and constant factors. We compared the two algorithms on 30 benchmark designs, on a Sun UltraSparc-60 workstation. On average Forge is 200 times faster than the previously published algorithm. We then improved Forge by incorporating a slew-rate-based convex delay model, which handles distinct rise and fall gate delays. We show that Forge is 15 times faster, on average, than the AMPS transistor-sizing tool from Synopsys, while achieving the same delay targets and using similar total transistor area.