Collaborative Research: Planning Grant: I/UCRC for Advanced Electronics through Machine Learning

合作研究：规划补助金：I/UCRC 通过机器学习实现先进电子学

• 批准号: 1464544
• 负责人: Elyse Rosenbaum
• 金额: $ 1.63万
• 依托单位: University of Illinois at Urbana-Champaign
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-04-15 至 2016-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1464544&HistoricalAwards=false
• 关键词: Collaborative; Research; Planning; Grant; UCRC

The accepted engineering design methodology requires that mass scale manufacturing of a new product not commence until a prototype of the product is tested and found to meet its performance specifications. It is not unusual for a product to go through multiple design iterations before it can satisfy all the design requirements. Modern electronic products, which range from a single integrated circuit to a smart phone to an aircraft instrumentation system, are so complex and contain so many components - billions in the case of an integrated circuit - that it is infeasible to construct hardware prototypes for each design iteration, from the points of view of both cost and time. Instead, a mathematical representation of the product must be developed, i.e. a virtual prototype, and its behavior then simulated. Each of the components that constitute the product would be represented by a model. Behavioral models of the components are most desirable; a behavioral model represents the terminal response of a component in response to an outside stimulus or signal, without concern to the inner workings of the component. Behavioral models are computationally efficient and have the benefit of obscuring intellectual property. However, despite many years of significant effort by the electronic design automation community, there is not a general, systematic method to generate accurate and comprehensive behavioral models, in part because of the non-linear, complex and multi-port nature of the components being modeled. The proposing team will utilize the planning grant to establish a research center that will overcome these modeling challenges through the development and application of novel machine-learning methods and algorithms.Machine-learning algorithms are used to extract a model of a component or system from input-output data, despite the presence of uncertainty and noise. In this center, the input-output data are obtained either from measurements of a component or by running detailed simulations of a component. The emphasis is on models that balance good predictive ability against computational complexity. The center will pioneer the application of machine learning to electronics modeling. It will develop a methodology to use prior knowledge, i.e., physical constraints and domain knowledge provided by designers, to speed up the learning process. Novel methods of incorporating component variability, including that due to semiconductor process variations, will be developed.

公认的工程设计方法要求，在新产品的原型经过测试并发现符合其性能规格之前，不能开始大规模生产新产品。一个产品要经过多次设计迭代才能满足所有的设计要求，这并不少见。现代电子产品，从单个集成电路到智能手机再到飞机仪表系统，非常复杂，包含如此多的组件--集成电路的情况下有数十亿个--从成本和时间的角度来看，为每一次设计迭代构建硬件原型是不可行的。相反，必须开发产品的数学表示，即虚拟原型，然后模拟其行为。构成产品的每个组件都将由一个模型表示。组件的行为模型是最理想的；行为模型表示组件对外部刺激或信号的最终响应，而不考虑组件的内部工作。行为模型在计算上是高效的，并且具有模糊知识产权的好处。然而，尽管电子设计自动化社区多年来做出了重大努力，但还没有一种通用的、系统的方法来生成准确和全面的行为模型，部分原因是被建模的组件具有非线性、复杂和多端口的性质。提案团队将利用规划拨款建立一个研究中心，通过开发和应用新的机器学习方法和算法来克服这些建模挑战。机器学习算法用于从输入输出数据中提取组件或系统的模型，尽管存在不确定性和噪声。在这个中心，输入-输出数据要么来自组件的测量，要么通过运行组件的详细模拟来获得。重点是在良好的预测能力和计算复杂性之间取得平衡的模型。该中心将率先将机器学习应用于电子建模。它将制定一种方法，利用先前的知识，即设计师提供的物理限制和领域知识，来加快学习过程。将开发结合元件可变性的新方法，包括由于半导体工艺变化而引起的可变性。