CIF: SMALL: Information Theoretic Multi-Core Processor Thermal Profile Estimation

CIF：SMALL：信息论多核处理器热分布估计

• 批准号: 0917057
• 负责人: Ankur Srivastava
• 金额: $ 45万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-08-01 至 2015-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0917057&HistoricalAwards=false
• 关键词: CIF; SMALL; Information; Theoretic; Multi

Dynamic thermal management is the process of controlling surges in the operating temperature especially of a multi-core processor during runtime, based on limited measurements by on-chip thermal sensors. Managing thermal sensors and processing their measurements presents a rich vein of theoretical and practical challenges including: deciding on the number, location and type of thermal sensors; estimating the thermal profile; and characterizing the fundamental performance tradeoffs between sensor quantity and complexity in guaranteeing estimate accuracy. In this interdisciplinary project, we propose a new approach to the problem of thermal profile estimation in multi-core processors which relies on fundamental information theoretic principles. Our approach rests on new problems in information theory that capture the salient features of on-chip thermal profile estimation. Our new formulations are inspired notably by rate distortion theory and also bear a similarity to compressed sensing. Furthermore, our approach has wider applicability to general problems of parameter estimation based on limited sampled and quantized measurements.The project has a home in computer architecture, VLSI design as well as one in information theory and compressed sensing. Its potential impact is twofold: (a) improvement in the performance and reliability of multi-core processors; and (b) new models and problem formulations in the fields of information theory and compressed sensing. The broad reach of this project will provide a valuable learning environment for the investigators and their graduate and undergraduate students. The basic elements of the technical approach will be discussed in Special Topics courses and seminars with the participation of graduate students.

动态热管理是基于片上热传感器的有限测量来控制运行温度（尤其是多核处理器的运行时温度）的浪涌的过程。管理热传感器和处理其测量结果提出了丰富的理论和实践挑战，包括：决定热传感器的数量，位置和类型;估计热分布;以及表征传感器数量和复杂性之间的基本性能权衡，以保证估计精度。在这个跨学科的项目中，我们提出了一种新的方法来解决多核处理器中的热分布估计问题，它依赖于基本的信息理论原理。我们的方法依赖于信息理论中的新问题，捕获片上热分布估计的显着特点。我们的新配方的启发，特别是率失真理论，也承担压缩感知的相似性。此外，我们的方法具有更广泛的适用性，以有限的采样和量化测量的基础上的参数估计的一般问题。该项目有一个家在计算机体系结构，超大规模集成电路设计，以及在信息论和压缩感知。其潜在影响有两个方面：（a）提高多核处理器的性能和可靠性;（B）在信息论和压缩感知领域提出新的模型和问题公式。该项目的广泛影响将为研究人员及其研究生和本科生提供一个有价值的学习环境。技术方法的基本要素将在研究生参与的专题课程和研讨会上讨论。