SHF: Small: Variability-Aware System-Level Power Management in Multi-Processor Systems

SHF：小型：多处理器系统中的可变性感知系统级电源管理

• 批准号: 1018980
• 负责人: Massoud Pedram
• 金额: $ 40万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1018980&HistoricalAwards=false
• 关键词: SHF; Small; Variability; Aware; System

With the increasing levels of variability in the characteristics of nanoscale CMOS devices and VLSI interconnects and continued uncertainty in the operating conditions of VLSI circuits, achieving power efficiency and high performance in electronic systems under process, voltage, and temperature variations as well as current stress, device aging, and interconnect wear-out phenomena has become a daunting, yet vital, task. This proposal tackles the problem of system-level dynamic power management (DPM) in systems which are manufactured in nanoscale CMOS technologies and are operated under widely varying conditions over the lifetime of the system. Such systems are greatly affected by increasing levels of process variations typically materializing as intrinsic (random) or systematic sources of variability and wearout/aging effects in device and interconnect characteristics, and widely varying workloads and temperature fluctuations usually appearing as sources of uncertainty. At the system level this variability and uncertainty is beginning to undermine the effectiveness of traditional DPM approaches. It is thus critically important that we develop the mathematical basis and practical applications of a variability-aware, uncertainty-reducing DPM approach with the following unique features and capabilities: Utilization of a two-tier stochastic modeling framework based on the theories of variability-sensitive, partially observable Markovian Decision Model and closed-loop feedback control theory, which can efficiently cope with variability and effectively reduce uncertainty in key system parameters. The framework also allows for self-learning (adaptive) policy optimization approaches, and multi-manager systems with multiple reward and cost rates for simultaneous optimization of the system energy consumption and performance.Successfully overcoming the challenges addressed by this project will result in significant energy savings for a typical server. Other impacts of this research includes the development of a new and powerful mathematical framework for resource management in complex and large systems that can deal with multiple-agents, multiple reward and cost rates and discount factors while accounting for effects of variability and simultaneously reducing the impact of uncertainty through measurements and sampling. The stochastic decision making framework with closed loop feedback control is also useful for solving a variety of other problems including dynamic thermal control, concurrent DPM and task scheduling in multi-core processor systems, consideration of total system?s energy efficiency, energy-efficient power delivery network design. If successful, the approach can result in a practical stochastic optimization framework for handling many important problems, ranging from energy efficiency improvement (and hence reduction in cost of operation) for electronics systems to data centers comprised of a large number of server/storage elements. Education, Outreach, and Training Programs include new curricula; recruiting under-represented students; research internship opportunities for undergraduates; and a Junior Scholars program for high school students.

随着纳米级CMOS设备和VLSI互连的特征的可变性水平不断提高，并且在VLSI电路的工作条件下持续不确定性，在流程，电压和温度变化以及当前的压力，设备凝聚和互连的耐磨效果的情况下，达到了电压和温度变化的电子系统中的功率效率和高性能。该提案解决了在纳米级CMOS技术中制造的系统中系统级动态功率管理（DPM）的问题，并在系统的一生中在较大的条件下操作。这些系统通常受到工艺变化水平的增加，通常呈现为固有（随机）或系统的可变性和磨损/老化效应的源和互连特征的衰老/老化效应，并且工作量的变化和温度波动通常是不确定性来源的。在系统级别，这种可变性和不确定性开始破坏传统DPM方法的有效性。因此，至关重要的是，我们开发具有以下独特功能和能力的可变性意识，降低不确定性的DPM方法的数学基础和实际应用是：利用两层随机建模框架，基于可变性的理论基于可变性的，可观察到的可观察到的可变性，有效地效率和封闭性反馈理论，这是可变性的，这可以降低有效的效率和有效的效率，从而降低了有效的效率和效率的cope cope，该系统可以降低。 参数。该框架还允许进行自学习（自适应）策略优化方法，并且具有多个奖励和成本率的多管理系统，以同时优化系统的能源消耗和性能。成功克服该项目所解决的挑战将导致典型服务器的大量能源节省。这项研究的其他影响包括在复杂和大型系统中开发一个新的和强大的数学框架，以处理多个代理，多重奖励和成本率和折扣因素，同时考虑可变性的影响，并同时通过测量和抽样来减少不确定性的影响。具有闭环反馈控制的随机决策框架也可用于解决多种其他问题，包括动态热控制，并发DPM和多核处理器系统中的任务计划，考虑总系统的能源效率，能源有效的电力传递网络设计。如果成功的话，该方法可能会导致一个实用的随机优化框架来处理许多重要问题，从提高能源效率（从而降低运行成本）到电子系统的数据中心到由大量服务器/存储元素组成的数据中心。教育，外展和培训计划包括新课程；招募人数不足的学生；研究本科生的研究实习机会；以及针对高中生的初级学者计划。