Effective Analytical Placement for Advanced Hybrid-Row-Height Circuit Designs

Recently, hybrid-row-height designs have been introduced to achieve performance and area co-optimization in advanced nodes. Hybrid-row-height designs incur challenging issues to layout due to the heterogeneous cell and row structures. In this paper, we present an effective algorithm to address the hybrid-row-height placement problem in two major stages: (1) global placement, and (2) legalization. Inspired by the multi-channel processing method in convolutional neural networks (CNN), we use the feature extraction technique to equivalently transform the hybrid-row-height global placement problem into two sub-problems that can be solved effectively. We propose a multi-layer nonlinear framework with alignment guidance and a self-adaptive parameter adjustment scheme, which can obtain a high-quality solution to the hybrid-row-height global placement problem. In the legalization stage, we formulate the hybrid-row-height legalization problem into a convex quadratic programming (QP) problem, then apply the robust modulus-based matrix splitting iteration method (RMMSIM) to solve the QP efficiently. After RMMSIM-based global legalization, Tetris-like allocation is used to resolve remaining physical violations. Compared with the state-of-the-art work, experiments on the 2015 ISPD Contest benchmarks show that our algorithm can achieve 7%; shorter final total wirelength and $2.23 \times $ speedup.