Collaborative Research: DDDAS-SMRP: Optimizing Signal and Image Processing in a Dynamic, Data-Driven Application System

合作研究：DDDAS-SMRP：在动态、数据驱动的应用系统中优化信号和图像处理

• 批准号: 0540154
• 负责人: Anna Gilbert
• 金额: $ 9万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-12-15 至 2008-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0540154&HistoricalAwards=false
• 关键词: Collaborative; Research; DDDAS; SMRP; Optimizing

This project will develop a dynamic, data-driven application system for signal and image processing under resource constraints. This system would lay a foundation for highly optimized implementations of fundamental signal and imaging processing computations that arise in many science and engineering problems, including image recognition, communications analysis, speech processing, querying, indexing, and retrieval from multimedia databases, and image segmentation of aerial, satellite, and astronomical images. The proposed multidisciplinary approach optimizes from algorithm specification, to mathematical representation, to software and hardware (FPGA) implementation, based on properties of data and unique requirements of the environment and the target hardware device. The novelty of the system is twofold: (1) it performs joint optimizationacross mathematical, software and hardware (system-on-a-chip FPGA) domains; and, (2) it is a dynamic, data-driven system in that signal-processing transforms are tailored to algorithm requirements and input signals, for reduced distortion and increased compression, and the system can be queried and steered during execution. Implementations are based on the best mathematical formulation of the problem coupled with automated selection of the best implementation among a space of alternatives, through the integration of models relating mathematical properties to implementationbehavior. Both hardware and software optimization are treated in a unified way. It is anticipated that with these methods the design-time will be decreased by two orders of magnitude or more, compared to implementations derived in a traditional way. Because the proposed system can explore a broad range of implementations that exceed the capabilities of a human designer, the implementations derived by the approach pursued in the project may even exhibit lower resource costs and higher performance. This research will provide a foundation for signal processing at all scales, providing key building blocks for engineers to build complex, distributed networks of adaptive signal processing sensors.

该项目将开发一个动态的、数据驱动的应用系统，用于在资源限制下进行信号和图像处理。该系统将为许多科学和工程问题中出现的基本信号和图像处理计算的高度优化实现奠定基础，包括图像识别、通信分析、语音处理、多媒体数据库的查询、索引和检索，以及图像分割航空、卫星和天文图像。所提出的多学科方法根据数据属性以及环境和目标硬件设备的独特要求，从算法规范到数学表示，再到软件和硬件（FPGA）实现进行优化。该系统的新颖性有两个：（1）它在数学、软件和硬件（片上系统 FPGA）领域进行联合优化； (2)它是一个动态的数据驱动系统，信号处理变换是根据算法要求和输入信号定制的，以减少失真和增加压缩，并且系统可以在执行期间查询和引导。实现基于问题的最佳数学公式，并通过将数学属性与实现行为相关的模型集成，在一系列替代方案中自动选择最佳实现。硬件和软件优化都是统一对待的。预计与以传统方式获得的实现相比，通过这些方法，设计时间将减少两个数量级或更多。由于所提出的系统可以探索超出人类设计人员能力的广泛实现，因此通过项目中所追求的方法得出的实现甚至可以表现出更低的资源成本和更高的性能。这项研究将为所有规模的信号处理奠定基础，为工程师构建复杂的分布式自适应信号处理传感器网络提供关键构建模块。