Analytical Heterogeneous Die-to-Die 3-D Placement With Macros

Yuxuan Zhao, Peiyu Liao, Siting Liu, Jiaxi Jiang, Yibo Lin, Bei Yu

Abstract: This article presents an innovative approach to 3-D mixed-size placement in heterogeneous face-to-face (F2F) bonded 3-D ICs. We propose an analytical framework that utilizes a dedicated density model and a bistratal wirelength model, effectively handling macros and standard cells in a 3-D solution space. A novel 3-D preconditioner is developed to resolve the topological and physical gap between macros and standard cells. Additionally, we propose a mixed-integer linear programming (MILP) formulation for macro rotation to optimize wirelength. Our framework is implemented with full-scale GPU acceleration, leveraging an adaptive 3-D density accumulation algorithm and an incremental wirelength gradient algorithm. Experimental results on ICCAD 2023 contest benchmarks demonstrate that our framework can achieve 5.9% quality score improvement compared to the first-place winner with 4.0 $\times $ runtime speedup. Additional experiments on modern RISC-V designs further validate the generalizability and superiority of our framework.