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

随着纳米级CMOS器件和VLSI互连的特性的可变性水平的增加以及VLSI电路的操作条件的持续不确定性，在工艺、电压和温度变化以及电流应力、器件老化和互连磨损现象下在电子系统中实现功率效率和高性能已经成为令人生畏但至关重要的任务。该建议解决了在纳米级CMOS技术中制造的系统中的系统级动态功率管理（dynamic power management，DVM）的问题，并且在系统的寿命期间在广泛变化的条件下操作。这样的系统受到工艺变化的增加的影响，工艺变化通常表现为器件和互连特性中的可变性和磨损/老化效应的固有（随机）或系统性来源，并且广泛变化的工作负载和温度波动通常表现为不确定性的来源。在系统级，这种可变性和不确定性开始破坏传统的可预测性方法的有效性。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感知，不确定性降低的方法的数学基础和实际应用：利用基于变量敏感、部分可观测马尔可夫决策模型和闭环反馈控制理论的双层随机建模框架，其可以有效地科普可变性并且有效地减少关键系统参数中的不确定性。该框架还允许自学习（自适应）策略优化方法，以及具有多个奖励和成本率的多管理器系统，用于同时优化系统能耗和性能。成功克服该项目所解决的挑战将导致典型服务器的显著节能。这项研究的其他影响包括开发一个新的和强大的数学框架，用于复杂和大型系统中的资源管理，可以处理多个代理，多个奖励和成本率和折扣因素，同时考虑可变性的影响，并同时通过测量和采样减少不确定性的影响。随机决策框架与闭环反馈控制也是有用的，用于解决各种其他问题，包括动态热控制，并发的并行处理器和任务调度在多核处理器系统，考虑整个系统？的能源效率，能源效率的电力输送网络设计。如果成功，该方法可以产生用于处理许多重要问题的实用随机优化框架，范围从电子系统的能源效率改进（并因此降低操作成本）到由大量服务器/存储元件组成的数据中心。教育，推广和培训计划包括新的课程;招募代表性不足的学生;本科生的研究实习机会;以及高中生的初级学者计划。

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.