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Energy Consumption in Networks on Chip: Efficiency and Scaling.

机译:片上网络的能耗:效率和规模。

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摘要

Computer architecture design is in a new era where performance is increased by replicating processing cores on a chip rather than making CPUs larger and faster. This design strategy is motivated by the superior energy efficiency of the multi-core architecture compared to the traditional monolithic CPU. If the trend continues as expected, the number of cores on a chip is predicted to grow exponentially over time as the density of transistors on a die increases.;A major challenge to the efficiency of multi-core chips is the energy used for communication among cores over a Network on Chip (NoC). As the number of cores increases, this energy also increases, imposing serious constraints on design and performance of both applications and architectures. Therefore, understanding the impact of different design choices on NoC power and energy consumption is crucial to the success of the multi- and many-core designs.;This dissertation proposes methods for modeling and optimizing energy consumption in multi- and many-core chips, with special focus on the energy used for communication on the NoC. We present a number of tools and models to optimize energy consumption and model its scaling behavior as the number of cores increases. We use synthetic traffic patterns and full system simulations to test and validate our methods. Finally, we take a step back and look at the evolution of computer hardware in the last 40 years and, using a scaling theory from biology, present a predictive theory for power-performance scaling in microprocessor systems.
机译:计算机体系结构设计处于一个新时代,在该时代中,通过在芯片上复制处理内核而不是使CPU更大,更快,可以提高性能。与传统的单片CPU相比,这种设计策略是由多核体系结构的卓越能效推动的。如果这种趋势如预期的那样继续下去,则随着芯片上晶体管密度的增加,芯片上的核数预计会随着时间呈指数增长。多核芯片效率的主要挑战是用于各核之间通信的能量核心通过片上网络(NoC)。随着内核数量的增加,这种能量也增加了,这对应用程序和体系结构的设计和性能构成了严重的限制。因此,了解不同设计选择对NoC功耗和能耗的影响对于多核和多核设计的成功至关重要。本论文提出了建模和优化多核和多核芯片能耗的方法,特别关注NoC上用于通信的能量。我们提出了许多工具和模型来优化能耗,并随着堆芯数量的增加对其扩展行为进行建模。我们使用综合流量模式和完整的系统模拟来测试和验证我们的方法。最后,我们退后一步,回顾过去40年中计算机硬件的发展,并使用生物学中的缩放理论,提出了微处理器系统中功率性能缩放的预测理论。

著录项

  • 作者

    Bezerra, George B. P.;

  • 作者单位

    The University of New Mexico.;

  • 授予单位 The University of New Mexico.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2012
  • 页码 147 p.
  • 总页数 147
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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