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

本项目将在资源有限的情况下开发一个动态的、数据驱动的信号和图像处理应用系统。该系统将奠定基础，高度优化的基本信号和成像处理计算，出现在许多科学和工程问题，包括图像识别，通信分析，语音处理，查询，索引和检索多媒体数据库，以及航空，卫星和天文图像的图像分割。所提出的多学科方法优化算法规范，数学表示，软件和硬件（FPGA）的实施，基于数据的属性和环境和目标硬件设备的独特要求。该系统的新奇有两个方面：（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，因为信号处理变换是根据算法要求和输入信号量身定制的，以减少失真和增加压缩，并且可以在执行期间查询和操纵系统。实现是基于最好的数学公式的问题，再加上自动选择的最佳实现之间的空间的替代品，通过集成的模型相关的数学属性implementationbehavior。硬件和软件优化都以统一的方式处理。可以预见，与以传统方式获得的实现相比，使用这些方法，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师能力的广泛实现，因此项目中所追求的方法所获得的实现甚至可以表现出更低的资源成本和更高的性能。这项研究将为所有尺度的信号处理提供基础，为工程师构建复杂的自适应信号处理传感器分布式网络提供关键构建模块。

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.