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

该项目将开发一个动态的、数据驱动的应用系统，用于在资源限制下的信号和图像处理。该系统将为在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，以及航空，卫星和天文图像的图像分割。提出的多学科方法从算法规范到数学表示，再到软件和硬件（FPGA）实现，基于数据的属性和环境和目标硬件设备的独特要求进行优化。该系统的新颖性是双重的：(1)它执行跨数学，软件和硬件（片上系统FPGA）领域的联合优化；(2)它是一个动态的、数据驱动的系统，信号处理变换根据算法要求和输入信号进行定制，以减少失真和增加压缩，并且在执行过程中可以查询和操纵系统。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，在备选空间中自动选择最佳实现。硬件优化和软件优化是统一处理的。与以传统方式派生的实现相比，预计使用这些方法的设计时间将减少两个数量级或更多。由于所建议的系统可以探索超出人类设计人员能力的广泛实现范围，因此由项目中所追求的方法派生的实现甚至可能表现出更低的资源成本和更高的性能。这项研究将为所有尺度的信号处理提供基础，为工程师构建复杂的、分布式的自适应信号处理传感器网络提供关键的构建模块。

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.