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Profile- and instrumentation- driven methods for embedded signal processing.

机译:轮廓和仪器驱动的嵌入式信号处理方法。

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摘要

Modern embedded systems for digital signal processing (DSP) run increasingly sophisticated applications that require expansive performance resources, while simultaneously requiring better power utilization to prolong battery-life. Achieving such conflicting objectives requires innovative software/hardware design space exploration spanning a wide-array of techniques and technologies that offer trade-offs among performance, cost, power utilization, and overall system design complexity. To save on non-recurring engineering (NRE) costs and in order to meet shorter time-to-market requirements, designers are increasingly using an iterative design cycle and adopting model-based computer-aided design (CAD) tools to facilitate analysis, debugging, profiling, and design optimization.;In this dissertation, we present several profile- and instrumentation-based techniques that facilitate design and maintenance of embedded signal processing systems:;1. We propose and develop a novel, translation lookaside buffer (TLB) preloading technique. This technique, called context-aware TLB preloading (CTP), uses a synergistic relationship between the (1) compiler for application specific analysis of a task's context, and (2) operating system (OS), for run-time introspection of the context and efficient identification of TLB entries for current and future usage. CTP works by (1) identifying application hotspots using compiler-enabled (or manual) profiling, and (2) exploiting well-understood memory access patterns, typical in signal processing applications, to preload the TLB at context switch time. The benefits of CTP in eliminating inter-task TLB interference and preemptively allocating TLB entries during context-switch are demonstrated through extensive experimental results with signal processing kernels.;2. We develop an instrumentation-driven approach to facilitate the conversion of legacy systems, not designed as dataflow-based applications, to dataflow semantics by automatically identifying the behavior of the core actors as instances of well-known dataflow models. This enables the application of powerful dataflow-based analysis and optimization methods to systems to which these methods have previously been unavailable. We introduce a generic method for instrumenting dataflow graphs that can be used to profile and analyze actors, and we use this instrumentation facility to instrument legacy designs being converted and then automatically detect the dataflow models of the core functions. We also present an iterative actor partitioning process that can be used to partition complex actors into simpler entities that are more prone to analysis. We demonstrate the utility of our proposed new instrumentation-driven dataflow approach with several DSP-based case studies.;3. We extend the instrumentation technique discussed in (2) to introduce a novel tool for model-based design validation called dataflow validation framework (DVF). DVF addresses the problem of ensuring consistency between (1) dataflow properties that are declared or otherwise assumed as part of dataflow-based application models, and (2) the dataflow behavior that is exhibited by implementations that are derived from the models. The ability of DVF to identify disparities between an application's formal dataflow representation and its implementation is demonstrated through several signal processing application development case studies.
机译:用于数字信号处理(DSP)的现代嵌入式系统运行着越来越复杂的应用程序,这些应用程序需要扩展的性能资源,同时还需要更好的电源利用率以延长电池寿命。要实现这些相互矛盾的目标,就需要进行创新的软件/硬件设计空间探索,该探索涉及多种技术,这些技术可以在性能,成本,功耗和整体系统设计复杂性之间进行权衡。为了节省非经常性工程(NRE)成本并满足更短的上市时间要求,设计人员越来越多地使用迭代设计周期,并采用基于模型的计算机辅助设计(CAD)工具来促进分析,调试,性能分析和设计优化。;本文提出了几种基于配置文件和仪器的技术,这些技术有助于嵌入式信号处理系统的设计和维护:1。我们提出并开发了一种新颖的翻译后备缓冲区(TLB)预加载技术。这项技术称为上下文感知TLB预加载(CTP),它使用(1)编译器(用于任务上下文的应用程序特定分析)和(2)操作系统(OS)之间的协同关系,以对上下文进行运行时自省并有效地识别当前和将来使用的TLB条目。 CTP的工作原理是(1)使用启用编译器的(或手动)分析来识别应用程序热点,以及(2)利用在信号处理应用程序中常见的,易于理解的内存访问模式在上下文切换时预加载TLB。通过使用信号处理内核的大量实验结果,证明了CTP在消除任务间TLB干扰和在上下文切换过程中抢先分配TLB条目的好处。2。我们开发了一种仪器驱动的方法,通过自动将核心参与者的行为识别为众所周知的数据流模型的实例,来促进将不是基于数据流的应用程序设计的传统系统转换为数据流语义。这样就可以将强大的基于数据流的分析和优化方法应用于以前无法使用这些方法的系统。我们介绍了一种通用的数据流图检测方法,该方法可用于分析和分析参与者,并使用此检测工具来检测正在转换的旧式设计,然后自动检测核心功能的数据流模型。我们还提出了一个迭代的actor划分过程,该过程可用于将复杂的actor划分为更易于分析的简单实体。通过几个基于DSP的案例研究,我们证明了我们提出的新的仪器驱动数据流方法的实用性; 3。我们扩展了(2)中讨论的检测技术,以引入一种称为数据流验证框架(DVF)的新颖的基于模型的设计验证工具。 DVF解决了确保以下方面的一致性的问题:(1)已声明或以其他方式假定为基于数据流的应用程序模型的一部分的数据流属性,以及(2)从模型派生的实现所表现出的数据流行为。通过几个信号处理应用程序开发案例研究,证明了DVF识别应用程序的正式数据流表示形式及其实现之间的差异的能力。

著录项

  • 作者

    Chukhman, Ilya.;

  • 作者单位

    University of Maryland, College Park.;

  • 授予单位 University of Maryland, College Park.;
  • 学科 Computer engineering.;Electrical engineering.
  • 学位 Ph.D.
  • 年度 2015
  • 页码 146 p.
  • 总页数 146
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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