SHF: Small: Pattern-Aware Design

SHF：小型：模式感知设计

• 批准号: 1422516
• 负责人: Resit Sendag
• 金额: $ 45万
• 依托单位: University of Rhode Island
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-07-15 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1422516&HistoricalAwards=false
• 关键词: SHF; Small; Pattern; Aware; Design

Current processors employ aggressive prediction mechanisms to improve performance and reduce power. To design effective optimizations, it is increasingly important to understand and quantify a program's dynamic behavior. However, today, most of these optimizations rely on heuristics and are fairly ad-hoc. After some patterns are observed, a hardware decision is made and the design space of the hardware optimization is explored through simulation to determine the best performing configuration. However, because the design is targeted for observed and/or anticipated patterns, some dynamic behavior is not captured and remains undetected.This project develops a framework for quantitatively analyzing a program's behavior in terms of regularities and patterns to provide insights into the design of next-generation hardware prediction mechanisms. Inspired by DNA discovery tools, the research adopts algorithms used in string processing, compression and bioinformatics, in order to summarize applications' dynamic data reference and branch outcome behaviors. The findings from the methods and tools developed in this project will have a big impact on the design and efficiency of future hardware components. The PI will collaborate with industry partners to further increase the broader impact. The research will engage graduate and undergraduate students and promote the participation of underrepresented groups.

当前的处理器采用积极的预测机制来提高性能并降低功率。为了设计有效的优化，了解和量化程序的动态行为越来越重要。但是，如今，大多数这些优化都取决于启发式方法，并且是相当临时的。观察到一些模式后，制定了硬件决策，并通过仿真探索硬件优化的设计空间，以确定最佳性能配置。但是，由于设计针对观察和/或预期的模式，因此未捕获某些动态行为，并且仍未被发现。该项目开发了一个框架，以定量分析程序的行为，从规律性和模式方面进行洞察力，以提供有关下一代硬件硬件预测机制的设计。受DNA发现工具的启发，该研究采用了在弦处理，压缩和生物信息学中使用的算法，以总结应用程序的动态数据参考和分支结果行为。该项目中开发的方法和工具的发现将对未来硬件组件的设计和效率产生重大影响。 PI将与行业合作伙伴合作，以进一步增加更广泛的影响。这项研究将吸引研究生和本科生，并促进代表性不足的群体的参与。