Optimal Sequential and Multilevel Combinational Logic Synthesis

最优顺序和多级组合逻辑综合

• 批准号: 8719546
• 负责人: Gary Hachtel
• 金额: $ 199.3万
• 依托单位: University of Colorado at Boulder
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 1988
• 资助国家: 美国
• 起止时间: 1988-09-01 至 1992-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=8719546&HistoricalAwards=false
• 关键词: Optimal; Sequential; Multilevel; Combinational; Logic

This is a group effort involving researchers at the University of Colorado, Boulder (Gary Hachtel and M. Lightner), at the University of California, Berkeley (R. K. Brayton, A. R. Newton and A. Sangiovanni-Vincentelli), and at Stanford University (G. De Micheli). The long-range goal is understanding the theoretical structure needed for optimization of VLSI (very large scale integrated) design parameters in novel (application- specific) applications. The approach is to explore unified and systematic methods for both combination and sequential logic synthesis and minimization, which includes research on behavioral and structural synthesis tasks at higher levels of abstraction. Specific objectives are: (1) performance oriented synthesis (e.g. optimizing the delay/area tradeoff); (2) maximization of testability; and (3) extension of size capability to VLSI-sized problems. A comprehensive set of theorems, algorithms, mathematical models and their corresponding intermediate forms, and software tools will be developed. Central to the effort is utilization of their research paradigm involving a consistent mathematical treatment, optimization-based graph algorithms, and optimized mapping from one level of abstraction to the next.

这是科罗拉多大学博尔德分校(Gary Hachtel和M.Lightner)、加州大学伯克利分校(R.K.Brayton、A.R.牛顿和A.Sangovanni-Vincentelli)以及斯坦福大学(G.de Micheli)的研究人员共同参与的研究成果。长期目标是了解在新型(特定应用)应用中优化VLSI(超大规模集成)设计参数所需的理论结构。该方法是探索用于组合和顺序逻辑综合和最小化的统一和系统的方法，其中包括在更高抽象水平上对行为和结构综合任务的研究。具体目标是：(1)面向性能的综合(例如，优化延迟/面积折衷)；(2)最大化可测试性；以及(3)将尺寸能力扩展到VLSI大小的问题。将开发一套全面的定理、算法、数学模型及其相应的中间形式和软件工具。这项工作的核心是利用他们的研究范式，包括一致的数学处理、基于优化的图形算法，以及从一个抽象级别到另一个抽象级别的优化映射。