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: 1464539
• 负责人: Madhavan Swaminathan
• 金额: $ 1.15万
• 依托单位: Georgia Tech Research Corporation
• 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=1464539&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新产品的质量规模生产直到测试产品的原型并符合其性能规范后才开始。产品在满足所有设计要求之前进行多次设计迭代并不罕见。从单个集成电路到智能手机到飞机仪器系统的现代电子产品非常复杂，并且包含许多组件 - 在集成电路的情况下数十亿美元 - 从成本和时间的角度来看，为每种设计迭代构造硬件原型是不可避免的。取而代之的是，必须开发产品的数学表示，即虚拟原型及其行为。构成产品的每个组件都将由模型表示。组件的行为模型是最可取的。行为模型代表组件对外部刺激或信号的末端响应，而不必关注组件的内部工作。行为模型在计算上是有效的，并且具有掩盖知识产权的好处。然而，尽管电子设计自动化社区多年来付出了多年的努力，但没有一种通用的，系统的方法来产生准确，全面的行为模型，部分原因是要建模的组件的非线性，复杂和多端口性质。提议的团队将利用计划赠款来建立一个研究中心，该研究中心将通过开发和应用新颖的机器学习方法和算法来克服这些建模挑战。尽管存在不确定和噪音，但使用新的机器学习方法和算法来从输入输出数据中提取组件或系统的模型。在此中心，输入输出数据是从组件的测量值或通过对组件的详细模拟获得的。重点是平衡良好的预测能力与计算复杂性的模型。该中心将开拓机器学习到电子产品建模的应用。它将开发一种方法来使用先验知识，即设计师提供的物理约束和领域知识，以加快学习过程。将开发合并组件变异性的新方法，包括由于半导体过程变化而发展。