PARSAC:为具有复杂设计约束条件的现代 SoC 进行快速、人性化的平面规划

Hesham Mostafa, Uday Mallappa, Mikhail Galkin, Mariano Phielipp, Somdeb Majumdar
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引用次数: 0

摘要

芯片上系统(SoC)和芯片子系统的平面规划是物理设计流程中至关重要的一步,因为它决定了组成系统的模块的最佳形状和位置。模拟退火(SA)一直是解决经典平面规划问题的首选方法,其目标是最大限度地减少线长和总放置面积。然而,工业相关平面规划问题的目标不仅是最大限度地减少面积和线长,而且还要遵守硬放置约束,这些约束规定了某些模块的总体面积和/或特定放置位置。我们的研究表明,简单地将这些约束条件纳入 SA 目标函数会导致次优解,而且往往是非法解。我们提出了 "约束感知模拟退火"(CA-SA)方法,并证明该方法在处理无位置约束的平面规划问题时,性能大大优于香草退火法。我们在 CA-SA 的基础上开发了一种新的平面规划工具:PARSAC(带约束条件的并行模拟退火)。PARSAC 是一种高效、易用和大规模并行的平面规划工具。PARSAC 在传统平面规划基准上的表现也优于传统的 SA。PARSAC 是一个开放源代码库,可供研究人员在我们的成果基础上进行复制和构建。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PARSAC: Fast, Human-quality Floorplanning for Modern SoCs with Complex Design Constraints
The floorplanning of Systems-on-a-Chip (SoCs) and of chip sub- systems is a crucial step in the physical design flow as it determines the optimal shapes and locations of the blocks that make up the system. Simulated Annealing (SA) has been the method of choice for tackling classical floorplanning problems where the objective is to minimize wire-length and the total placement area. The goal in industry-relevant floorplanning problems, however, is not only to minimize area and wire-length, but to do that while respecting hard placement constraints that specify the general area and/or the specific locations for the placement of some blocks. We show that simply incorporating these constraints into the SA objective function leads to sub-optimal, and often illegal, solutions. We propose the Constraints-Aware Simulated Annealing (CA-SA) method and show that it strongly outperforms vanilla SA in floorplanning problems with hard placement constraints. We developed a new floorplan- ning tool on top of CA-SA: PARSAC (Parallel Simulated Annealing with Constraints). PARSAC is an efficient, easy-to-use, and mas- sively parallel floorplanner. Unlike current SA-based or learning- based floorplanning tools that cannot effectively incorporate hard placement-constraints, PARSAC can quickly construct the Pareto- optimal legal solutions front for constrained floorplanning problems. PARSAC also outperforms traditional SA on legacy floorplanning benchmarks. PARSAC is available as an open-source repository for researchers to replicate and build on our result.
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