Advanced Large-Scale Optimization Approaches to Solve Modern Circuit Layout Problems

解决现代电路布局问题的先进大规模优化方法

• 批准号: RGPIN-2016-03833
• 负责人: Vannelli, Anthony
• 金额: $ 1.75万
• 依托单位: Wilfrid Laurier University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=753201
• 关键词: Advanced; Large; Scale; Optimization; Approaches

The design of many fundamental manufacturing and circuit layout problems can be modeled as linear or nonlinear combinatorial optimization problems. All of these problems are NP-hard. Very tight performance specifications for these problems (i.e., minimum wirelength, minimum area, minimum power and congestion) demand near optimal designs subject to many millions to billions of variables and constraints. Over the last six years, we have developed efficient optimization techniques that can be used to solve these problems using interior point and semidefinite programming approaches that can be solved in polynomial time.There are two main objectives in developing this research program and direction. First, a major objective of the proposed research program is aimed at "integration" of large-scale interior point methodologies used in linear, quadratic, convex, second-order cone programming to form the basis of generating relative placements and routings with "little or no overlap" while reducing wirelength and area. A second major objective is to develop promising approaches to "allow optimizers to scale"; that is, predictable fast running times will be achieved as problem size increases beyond a million constraints and variables for these present day layout problems. To achieve these objectives during the next five years, two novel approaches to solve large optimization problems containing more than a million variables and constraints will be developed. First, recent advances in "matrix free" interior-point approaches will allow the scope and size of solved problems to be in the order of millions or billions of variables and constraints. Second, we plan to accelerate interior-point algorithms using new warmstarting techniques to speed up the running time of for these very large problems. Initial focus will be on the generation of Very Large Scale Integrated (VLSI) circuit layout for "standard cell" and "mixed cell" technologies that still form a major part of integrated circuit design. Second, the exploration of analytic-based models and large-scale nonlinear interior-point solvers will be developed to solve emerging large placement problems arising in Field Programmable Gate Array (FPGAs) layout. Another equally important "objective" of this research is to reduce the need for search techniques such as simulated annealing, genetic algorithms and Tabu search to further reduce wirelength and area as well as timing, delay, power and congestion problems. In this overall research program, we plan to minimally use search techniques to refine feasible starting solutions that are generated by the powerful interior point or semidefinite programming solvers. The aim is to use mathematical programming models of placement, floorplanning and global routing and to solve them as efficiently as possible to reduce or ideally eliminate the need for search techniques.

许多基本的制造和电路布局问题的设计可以建模为线性或非线性组合优化问题。这些问题都是NP难问题。针对这些问题的非常严格的性能规范（即，最小线长、最小面积、最小功率和拥塞）要求接近最优设计，受到数百万到数十亿的变量和约束。在过去的六年里，我们已经开发出有效的优化技术，可以用来解决这些问题，使用内点和半定规划方法，可以在多项式时间内解决。首先，拟议的研究计划的一个主要目标是在“集成”的线性，二次，凸，二阶锥规划中使用的大规模内点方法，形成的基础上产生的相对位置和布线“很少或没有重叠”，同时减少线长和面积。第二个主要目标是开发有前途的方法，以“允许优化规模”，也就是说，可预测的快速运行时间将实现问题的大小增加超过一百万的约束和变量，这些目前的布局问题。为了在未来五年内实现这些目标，将开发两种新的方法来解决包含一百多万个变量和约束的大型优化问题。首先，“无矩阵”边界点方法的最新进展将允许解决的问题的范围和大小在数百万或数十亿的变量和约束的顺序。其次，我们计划使用新的热启动技术来加速边界点算法，以加快这些非常大的问题的运行时间。 最初的重点将是为“标准单元”和“混合单元”技术生成超大规模集成（VLSI）电路布局，这些技术仍然是集成电路设计的主要部分。其次，探索基于分析的模型和大规模非线性边界点求解器将开发解决现场可编程门阵列（FPGA）布局中出现的大型布局问题。 这项研究的另一个同样重要的“目标”是减少对模拟退火、遗传算法和禁忌搜索等搜索技术的需求，以进一步减少线长和面积以及定时、延迟、功耗和拥堵问题。在这个整体的研究计划中，我们计划最小限度地使用搜索技术来细化由强大的内点或半定规划求解器生成的可行的初始解。其目的是使用数学规划模型的布局，布局规划和全局布线，并解决他们尽可能有效地减少或理想地消除需要搜索技术。