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，在对产品原型进行测试并发现其满足其性能规格之前，不得开始大规模制造新产品。一款产品在满足所有设计要求之前要经过多次设计迭代的情况并不罕见。现代电子产品的范围从单个集成电路到智能手机再到飞机仪表系统，非常复杂并且包含如此多的组件（集成电路的组件数量达到数十亿），以至于为每个设计构建硬件原型是不可行的迭代，从成本和时间的角度来看。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；行为模型表示组件响应外部刺激或信号的最终响应，而不关心组件的内部工作原理。行为模型计算效率高，并且具有隐藏知识产权的优点。然而，尽管电子设计自动化界多年来付出了巨大的努力，但仍然没有一种通用的、系统的方法来生成准确和全面的行为模型，部分原因是组件的非线性、复杂和多端口特性。建模。提议团队将利用规划拨款建立一个研究中心，通过开发和应用新颖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中提取组件或系统的模型-输出数据，尽管存在不确定性和噪声。在该中心，输入输出数据是通过组件的测量或通过运行组件的详细模拟来获得的。重点是平衡良好的预测能力和计算复杂性的模型。该中心将率先将机器学习应用于电子建模。它将开发一种方法来使用先验知识，即设计人员提供的物理约束和领域知识，以加快学习过程。将开发结合组件可变性（包括由于半导体工艺变化而引起的可变性）的新方法。