Innovative 3D Mixed-Size Placement for Heterogeneous F2F Bonded ICs with Macros

Основные понятия

Proposing an innovative approach to 3D mixed-size placement in heterogeneous F2F bonded ICs, optimizing wirelength and handling macros efficiently.

Аннотация

The paper introduces a novel analytical framework for 3D mixed-size placement in heterogeneous face-to-face (F2F) bonded ICs. It addresses the challenges of integrating standard cells and macros in a 3D solution space. The proposed approach includes a dedicated density model, bistratal wirelength model, and macro rotation optimization using MILP formulation. Full-scale GPU acceleration is leveraged for efficient implementation. Experimental results show significant quality score improvement compared to existing methods.

As technology scaling reaches its limits, 3D integrated circuits (ICs) offer a solution by stacking multiple dies vertically. Heterogeneous 3D ICs can benefit from advanced technology nodes for standard cells without worrying about hard IPs' technology node.
Three main variants of 3D ICs are through-silicon-via (TSV) based, monolithic, and face-to-face (F2F) bonding.
Existing methodologies focus on either standard cell or mixed-size designs with macros. Recent placers for F2F bonded 3D ICs mainly concentrate on standard cell placement.
True-3D placers adopt analytical approaches to handle mixed-size placements effectively but lack accurate models for heterogeneous integration.
The proposed framework optimizes instance partitioning and locations in a 3D solution space while resolving topological and physical differences between macros and standard cells.
A novel preconditioner bridges the gap between macros and standard cells, enhancing convergence and runtime efficiency.

Статистика

Experimental results demonstrate a quality score improvement of up to 5.9% over the first-place winner with a runtime speedup of up to 4x.

Цитаты

Ключевые выводы из

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

by Yuxuan Zhao,... в arxiv.org 03-15-2024

https://arxiv.org/pdf/2403.09070.pdf

Дополнительные вопросы

How can this innovative approach be adapted to other types of integrated circuits

This innovative approach to 3D mixed-size placement in heterogeneous face-to-face bonded ICs can be adapted to other types of integrated circuits by modifying the density and wirelength models to suit the specific characteristics of those circuits. For example, for monolithic 3D integration, where fine-grained vertical interconnects are used, the density model could be adjusted to consider the different stacking configurations. Similarly, for through-silicon-via (TSV) based 3D ICs, modifications could be made to account for the larger pitches and parasitics associated with TSVs. By customizing the algorithms and models based on the requirements of different types of integrated circuits, this approach can be effectively applied across various architectures.

What potential challenges might arise when implementing this method at scale

Implementing this method at scale may present several challenges. One potential challenge is scalability - as the size and complexity of designs increase, computational resources required for optimization also grow significantly. Handling a large number of instances, nets, and macros while maintaining efficiency in runtime becomes crucial. Additionally, ensuring robustness and accuracy in placement results across diverse designs poses another challenge. Variability in design constraints, such as maximum utilization rates or macro area ratios among different circuits may require adaptive strategies within the algorithm to address these variations effectively.

How could advancements in machine learning impact the optimization process described in the article

Advancements in machine learning could have a significant impact on optimizing processes described in the article by enhancing decision-making capabilities during placement optimization. Machine learning techniques like reinforcement learning or neural networks can be utilized to learn patterns from previous placements and guide future decisions towards more optimal solutions efficiently. These advancements can help improve convergence speed, enhance solution quality by leveraging historical data insights into better partitioning strategies or rotation assignments based on learned patterns from similar designs.