ITR/NGS-Intelligent HW/SW Compilers for DSP Applications

ITR/NGS-用于 DSP 应用的智能硬件/软件编译器

• 批准号: 0325687
• 负责人: Jose Moura
• 金额: $ 140万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2010-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0325687&HistoricalAwards=false
• 关键词: ITR; NGS; Intelligent; HW; SW

We propose to develop a system to automate the design, implementation, and optimization of digital signal processing (DSP) applications for software (SW), hardware (HW), and SW/HW platforms with respect to multiple, possibly conflicting, performance metrics including speed, accuracy, area, and power. Ours is a new system of intelligent design and optimization tools that incorporate domainspecific knowledge of the mathematical structure of DSP computations and use this knowledge for automatic optimization and exploration of the design space, controlled by search and feedback optimization techniques. By including the 'high' mathematical level into the design automation, we achieve the following two main goals. The project will (1) overcome the known limitations of current optimizing compilers and synthesis tools that do not have access to high level structural information and are thus restricted to mechanical source code level manipulations, and will provide the framework to conquer for automation that step in the design flow that has traditionally been reserved to human ingenuity: intelligent algorithm manipulation and implementation to optimize desired performance metrics under given constraints.

我们建议开发一个系统来自动化数字信号处理（DSP）应用程序（SW），硬件（HW）和SW/HW平台的设计，实现和优化，相对于多个，可能相互冲突的性能指标，包括速度，准确性，区域和功率。我们的是一种新的智能设计和优化工具系统，它结合了DSP计算的数学结构的域特异性知识，并将这些知识用于自动优化和探索设计空间，并由搜索和反馈优化技术控制。通过将“高”数学级别包括在设计自动化中，我们实现了以下两个主要目标。 The project will (1) overcome the known limitations of current optimizing compilers and synthesis tools that do not have access to high level structural information and are thus restricted to mechanical source code level manipulations, and will provide the framework to conquer for automation that step in the design flow that has traditionally been reserved to human ingenuity: intelligent algorithm manipulation and implementation to optimize desired performance metrics under given constraints.