Analytical Heterogeneous Die-to-Die 3D Placement with Macros
CoRR(2024)
摘要
This paper presents an innovative approach to 3D mixed-size placement in
heterogeneous face-to-face (F2F) bonded 3D 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 3D solution space. A
novel 3D 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 3D density accumulation algorithm and an incremental
wirelength gradient algorithm. Experimental results on ICCAD 2023 contest
benchmarks demonstrate that our framework can achieve 5.9
improvement compared to the first-place winner with 4.0x runtime speedup.
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