CAREER: Data-flow Analysis in the Memory Management of Real-Time Multimedia Processing Systems

职业：实时多媒体处理系统内存管理中的数据流分析

• 批准号: 0133318
• 负责人: Florin Balasa
• 金额: $ 37.5万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-06-01 至 2007-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0133318&HistoricalAwards=false
• 关键词: CAREER; Data; flow; Analysis; Memory

This research focuses on devising novel techniques based on data-flow analysis in the memory management of real-time multidimensional signal processing. Data-flow analysis is the steering exploration mechanism along this project, allowing more exploration freedom than the traditional scheduling -based investigation, since the memory management tasks usually need only relative (rather than exact) lifetime information. Moreover, data-flow analysis enables the study of memory management tasks at the desired level of granularity -- between whole array and the scalar level -- trading-off computational effort and solution optimality.Part of this project investigates non-scalar methods for computing the memory size in real-time multimedia algorithms. This research addresses novel memory computation topics: dealing with a large class of parametric specifications, and dealing with parallelism in high-throughput applications. This project addresses also the problem of deriving a multilevel memory architecture optimized for area and/or power, subject to performance constraints. Another research direction is the optimized mapping of data from an embedded application code into the on-chip SRAM or the off-chip DRAM for maximizing the overall memory access performance of the application.The educational component of this research includes (1) the development of a graduate course covering algorithmic aspects of high-level synthesis and system design methodologies; (2) the development of a web tool for memory size computation.

本研究致力于在实时多维信号处理的内存管理中设计基于数据流分析的新技术。数据流分析是指导该项目的探索机制，与传统的基于调度的调查相比，它允许更多的探索自由，因为内存管理任务通常只需要相对(而不是准确)的生命周期信息。此外，数据流分析使得能够在所需的粒度级别上研究内存管理任务--在整个数组和标量级别之间--在计算工作量和解的优化之间进行权衡。本项目的一部分研究实时多媒体算法中计算内存大小的非标量方法。这项研究解决了新的内存计算主题：处理一大类参数规范，以及处理高吞吐量应用程序中的并行性。该项目还解决了在受性能约束的情况下，获得针对面积和/或功率进行优化的多级存储器体系结构的问题。另一个研究方向是将嵌入式应用程序代码中的数据优化映射到片上SRAM或片外DRAM中，以最大化应用程序的整体内存访问性能。本研究的教育内容包括(1)开发一门涵盖高级综合和系统设计方法学算法方面的研究生课程；(2)开发用于计算内存大小的Web工具。