Adaptive IC Design via Stochastic Optimization

ID：0702278标题：通过随机优化的自适应IC设计PI：Larry PileggiInst：Carnegie Mellon University摘要本项目的研究旨在解决在制造工艺和运行环境存在大规模变化的情况下集成电路的稳健设计。随着集成电路技术达到纳米级特征尺寸，由于制造和运行环境的影响，电路参数的波动显著增加。为了解决模拟和射频集成电路设计的这个特别具有挑战性的问题，我们提出了一种新的自适应设计方法来动态配置和调整电路，以适应所有可能的变化。该研究针对两个主要问题：(1)如何创建能够自适应地执行自测试和自配置的新的可调电路结构；(2)如何分析和优化这种自适应电路以探索性能和成本之间的折衷。我们将把制造和环境变化建模为随机变量，并从统计学中借用强大的数学方法来解决我们的自适应设计方法提出的这些独特的问题。这个项目的成功将刺激当今电子系统设计向随机设计的范式转变。这种方法最终可能使纳米芯片和生物芯片取得商业成果，这些芯片受到设计稳健性问题的严重限制。此外，这项工作将把适应性方法引入传统的工程课程，从而为学生提供具体的数学技术应用实例，这些技术对他们的长期职业生涯越来越重要。

ID: 0702278 Title: Adaptive IC Design via Stochastic OptimizationPI: Larry PileggiInst: Carnegie Mellon UniversityAbstractThe research of this project addresses the robust design of integrated circuits in the presence of large-scale variations from both the manufacturing process and the operating environment. As integrated circuit technologies reach nanoscale feature sizes, the fluctuations in circuit parameters due to manufacturing and operating environment increase significantly. To address this problem for the particularly challenging design of analog and radio frequency integrated circuits, we propose a new adaptive design methodology to dynamically configure and tune the circuit to accommodate all possible variations. Namely, our methodology will attempt to create circuits that offer self-adaptation to external changes and disturbances.The proposed research targets two major problems: (1) how to create new tunable circuit architectures that can adaptively perform self-testing and self-configuration, and (2) how to analyze and optimize such adaptive circuits to explore the tradeoff between performance and cost. We will model both manufacturing and environmental variations as random variables and borrow powerful mathematical methods from statistics to solve these unique problems as posed by our adaptive design methodology.The success of this project will stimulate a paradigm shift in today's electronic system design toward stochastic design. Such a methodology could ultimately enable nano-chips and bio-chips, which are significantly limited by design robustness issues, to reach commercial fruition. In addition, this work will bring adaptive methods into the traditional engineering curriculum, thereby providing the students with concrete application examples for mathematical techniques that are increasingly important for their long term careers.