Temperature Regulation in Multicore Processors Using Adjustable-Gain Integral Controllers

This paper considers the problem of temperature regulation in multicore processors by dynamic voltage-frequency scaling. We propose a feedback law that is based on an integral controller with adjustable gain, designed for fast tracking convergence in the face of model uncertainties, time-varying plants, and tight computing-timing constraints. Moreover, unlike prior works we consider a nonlinear, time-varying plant model that trades off precision for simple and efficient on-line computations. Cycle-level, full system simulator implementation and evaluation illustrates fast and accurate tracking of given temperature reference values, and compares favorably with fixed-gain controllers.Comment: 8 pages, 6 figures, IEEE Conference on Control Applications 2015, Accepted Versio

Dynamic thermal management in chip multiprocessor systems

Recently, processor power density has been increasing at an alarming rate result- ing in high on-chip temperature. Higher temperature increases current leakage and causes poor reliability. In our research, we ¯rst propose a Predictive Dynamic Ther- mal Management (PDTM) based on Application-based Thermal Model (ABTM) and Core-based Thermal Model (CBTM) in the multicore systems. Based on predicted temperature from ABTM and CBTM, the proposed PDTM can maintain the system temperature below a desired level by moving the running application from the possi- ble overheated core to the future coolest core (migration) and reducing the processor resources (priority scheduling) within multicore systems. Furthermore, we present the Thermal Correlative Thermal Management (TCDTM), which incorporates three main components: Statistical Workload Estimation (SWE), Future Temperature Estima- tion Model (FTEM) and Temperature-Aware Thread Controller (TATC), to model the thermal correlation e®ect and distinguish the thermal contributions from appli- cations with di®erent workload behaviors at run time in the CMP systems. The pro- posed PDTM and TCDTM enable the exploration of the tradeo® between throughput and fairness in temperature-constrained multicore systems

A Survey of Prediction and Classification Techniques in Multicore Processor Systems

In multicore processor systems, being able to accurately predict the future provides new optimization opportunities, which otherwise could not be exploited. For example, an oracle able to predict a certain application\u27s behavior running on a smart phone could direct the power manager to switch to appropriate dynamic voltage and frequency scaling modes that would guarantee minimum levels of desired performance while saving energy consumption and thereby prolonging battery life. Using predictions enables systems to become proactive rather than continue to operate in a reactive manner. This prediction-based proactive approach has become increasingly popular in the design and optimization of integrated circuits and of multicore processor systems. Prediction transforms from simple forecasting to sophisticated machine learning based prediction and classification that learns from existing data, employs data mining, and predicts future behavior. This can be exploited by novel optimization techniques that can span across all layers of the computing stack. In this survey paper, we present a discussion of the most popular techniques on prediction and classification in the general context of computing systems with emphasis on multicore processors. The paper is far from comprehensive, but, it will help the reader interested in employing prediction in optimization of multicore processor systems

Temperature Evaluation of NoC Architectures and Dynamically Reconfigurable NoC

Advancements in the field of chip fabrication led to the integration of a large number of transistors in a small area, giving rise to the multi–core processor era. Massive multi–core processors facilitate innovation and research in the field of healthcare, defense, entertainment, meteorology and many others. Reduction in chip area and increase in the number of on–chip cores is accompanied by power and temperature issues. In high performance multi–core chips, power and heat are predominant constraints. High performance massive multicore systems suffer from thermal hotspots, exacerbating the problem of reliability in deep submicron technologies. High power consumption not only increases the chip temperature but also jeopardizes the integrity of the system. Hence, there is a need to explore holistic power and thermal optimization and management strategies for massive on–chip multi–core environments. In multi–core environments, the communication fabric plays a major role in deciding the efficiency of the system. In multi–core processor chips this communication infrastructure is predominantly a Network–on–Chip (NoC). Tradition NoC designs incorporate planar interconnects as a result these NoCs have long, multi–hop wireline links for data exchange. Due to the presence of multi–hop planar links such NoC architectures fall prey to high latency, significant power dissipation and temperature hotspots. Networks inspired from nature are envisioned as an enabling technology to achieve highly efficient and low power NoC designs. Adopting wireless technology in such architectures enhance their performance. Placement of wireless interconnects (WIs) alters the behavior of the network and hence a random deployment of WIs may not result in a thermally optimal solution. In such scenarios, the WIs being highly efficient would attract high traffic densities resulting in thermal hotspots. Hence, the location and utilization of the wireless links is a key factor in obtaining a thermal optimal highly efficient Network–on–chip. Optimization of the NoC framework alone is incapable of addressing the effects due to the runtime dynamics of the system. Minimal paths solely optimized for performance in the network may lead to excessive utilization of certain NoC components leading to thermal hotspots. Hence, architectural innovation in conjunction with suitable power and thermal management strategies is the key for designing high performance and energy–efficient multicore systems. This work contributes at exploring various wired and wireless NoC architectures that achieve best trade–offs between temperature, performance and energy–efficiency. It further proposes an adaptive routing scheme which factors in the thermal profile of the chip. The proposed routing mechanism dynamically reacts to the thermal profile of the chip and takes measures to avoid thermal hotspots, achieving a thermally efficient dynamically reconfigurable network on chip architecture

Evaluation of temperature-performance trade-offs in wireless network-on-chip architectures

Continued scaling of device geometries according to Moore\u27s Law is enabling complete end-user systems on a single chip. Massive multicore processors are enablers for many information and communication technology (ICT) innovations spanning various domains, including healthcare, defense, and entertainment. In the design of high-performance massive multicore chips, power and heat are dominant constraints. Temperature hotspots witnessed in multicore systems exacerbate the problem of reliability in deep submicron technologies. Hence, there is a great need to explore holistic power and thermal optimization and management strategies for the massive multicore chips. High power consumption not only raises chip temperature and cooling cost, but also decreases chip reliability and performance. Thus, addressing thermal concerns at different stages of the design and operation is critical to the success of future generation systems. The performance of a multicore chip is also influenced by its overall communication infrastructure, which is predominantly a Network-on-Chip (NoC). The existing method of implementing a NoC with planar metal interconnects is deficient due to high latency, significant power consumption, and temperature hotspots arising out of long, multi-hop wireline links used in data exchange. On-chip wireless networks are envisioned as an enabling technology to design low power and high bandwidth massive multicore architectures. However, optimizing wireless NoCs for best performance does not necessarily guarantee a thermally optimal interconnection architecture. The wireless links being highly efficient attract very high traffic densities which in turn results in temperature hotspots. Therefore, while the wireless links result in better performance and energy-efficiency, they can also cause temperature hotspots and undermine the reliability of the system. Consequently, the location and utilization of the wireless links is an important factor in thermal optimization of high performance wireless Networks-on-Chip. Architectural innovation in conjunction with suitable power and thermal management strategies is the key for designing high performance yet energy-efficient massive multicore chips. This work contributes to exploration of various the design methodologies for establishing wireless NoC architectures that achieve the best trade-offs between temperature, performance and energy-efficiency. It further demonstrates that incorporating Dynamic Thermal Management (DTM) on a multicore chip designed with such temperature and performance optimized Wireless Network-on-Chip architectures improves thermal profile while simultaneously providing lower latency and reduced network energy dissipation compared to its conventional counterparts

Dynamic Thermal Management for Microprocessors

In deep submicron era, thermal hot spots and large temperature gradients significantly impact system reliability, performance, cost and leakage power. Dynamic thermal management techniques are designed to tackle the problems and control the chip temperature as well as power consumption. They refer to those techniques which enable the chip to autonomously modify the task execution and power dissipation characteristics so that lower-cost cooling solutions could be adopted while still guaranteeing safe temperature regulation. As long as the temperature is regulated, the system reliability can be improved, leakage power can be reduced and cooling system lifetime can be extended significantly. Multimedia applications are expected to form the largest portion of workload in general purpose PC and portable devices. The ever-increasing computation intensity of multimedia applications elevates the processor temperature and consequently impairs the reliability and performance of the system. In this thesis, we propose to perform dynamic thermal management using reinforcement learning algorithm for multimedia applications. The presented learning model does not need any prior knowledge of the workload information or the system thermal and power characteristics. It learns the temperature change and workload switching patterns by observing the temperature sensor and event counters on the processor, and finds the management policy that provides good performance-thermal tradeoff during the runtime. As the system complexity increases, it is more and more difficult to perform thermal management in a centralized manner because of state explosion and the overhead of monitoring the entire chip. In this thesis, we present a framework for distributed thermal management in many-core systems where balanced thermal profile can be achieved by proactive task migration among neighboring cores. The framework has a low cost agent residing in each core that observes the local workload and temperature and communicates with its nearest neighbor for task migration and exchange. By choosing only those migration requests that will result in balanced workload without generating thermal emergency, the presented framework maintains workload balance across the system and avoids unnecessary migration. Experimental results show that, our distributed management policy achieves almost the same performance as a global management policy when the tasks are initially randomly distributed. Compared with existing proactive task migration technique, our approach generates less hotspot, less migration overhead with negligible performance overhead. Temperature affects the leakage power and cooling power. In this thesis, we address the impact of task allocation on a processor\u27s leakage power and cooling fan power. Although the leakage power is determined by the average die temperature and the fan power is determined by the peak temperature, our analysis shows that the overall power can be minimized if a task allocation with minimum peak temperature is adopted together with an intelligent fan speed adjustment technique that finds the optimal tradeoff between fan power and leakage power. We further present a multi-agent distributed task migration technique that searches for the best task allocation during runtime. By choosing only those migration requests that will result chip maximum temperature reduction, the presented framework achieves large fan power savings as well as overall power reduction

Thermal-Aware Networked Many-Core Systems

Advancements in IC processing technology has led to the innovation and growth happening in the consumer electronics sector and the evolution of the IT infrastructure supporting this exponential growth. One of the most difficult obstacles to this growth is the removal of large amount of heatgenerated by the processing and communicating nodes on the system. The scaling down of technology and the increase in power density is posing a direct and consequential effect on the rise in temperature. This has resulted in the increase in cooling budgets, and affects both the life-time reliability and performance of the system. Hence, reducing on-chip temperatures has become a major design concern for modern microprocessors. This dissertation addresses the thermal challenges at different levels for both 2D planer and 3D stacked systems. It proposes a self-timed thermal monitoring strategy based on the liberal use of on-chip thermal sensors. This makes use of noise variation tolerant and leakage current based thermal sensing for monitoring purposes. In order to study thermal management issues from early design stages, accurate thermal modeling and analysis at design time is essential. In this regard, spatial temperature profile of the global Cu nanowire for on-chip interconnects has been analyzed. It presents a 3D thermal model of a multicore system in order to investigate the effects of hotspots and the placement of silicon die layers, on the thermal performance of a modern ip-chip package. For a 3D stacked system, the primary design goal is to maximise the performance within the given power and thermal envelopes. Hence, a thermally efficient routing strategy for 3D NoC-Bus hybrid architectures has been proposed to mitigate on-chip temperatures by herding most of the switching activity to the die which is closer to heat sink. Finally, an exploration of various thermal-aware placement approaches for both the 2D and 3D stacked systems has been presented. Various thermal models have been developed and thermal control metrics have been extracted. An efficient thermal-aware application mapping algorithm for a 2D NoC has been presented. It has been shown that the proposed mapping algorithm reduces the effective area reeling under high temperatures when compared to the state of the art.Siirretty Doriast

ADAPTIVE POWER MANAGEMENT FOR COMPUTERS AND MOBILE DEVICES

Power consumption has become a major concern in the design of computing systems today. High power consumption increases cooling cost, degrades the system reliability and also reduces the battery life in portable devices. Modern computing/communication devices support multiple power modes which enable power and performance tradeoff. Dynamic power management (DPM), dynamic voltage and frequency scaling (DVFS), and dynamic task migration for workload consolidation are system level power reduction techniques widely used during runtime. In the first part of the dissertation, we concentrate on the dynamic power management of the personal computer and server platform where the DPM, DVFS and task migrations techniques are proved to be highly effective. A hierarchical energy management framework is assumed, where task migration is applied at the upper level to improve server utilization and energy efficiency, and DPM/DVFS is applied at the lower level to manage the power mode of individual processor. This work focuses on estimating the performance impact of workload consolidation and searching for optimal DPM/DVFS that adapts to the changing workload. Machine learning based modeling and reinforcement learning based policy optimization techniques are investigated. Mobile computing has been weaved into everyday lives to a great extend in recent years. Compared to traditional personal computer and server environment, the mobile computing environment is obviously more context-rich and the usage of mobile computing device is clearly imprinted with user\u27s personal signature. The ability to learn such signature enables immense potential in workload prediction and energy or battery life management. In the second part of the dissertation, we present two mobile device power management techniques which take advantage of the context-rich characteristics of mobile platform and make adaptive energy management decisions based on different user behavior. We firstly investigate the user battery usage behavior modeling and apply the model directly for battery energy management. The first technique aims at maximizing the quality of service (QoS) while keeping the risk of battery depletion below a given threshold. The second technique is an user-aware streaming strategies for energy efficient smartphone video playback applications (e.g. YouTube) that minimizes the sleep and wake penalty of cellular module and at the same time avoid the energy waste from excessive downloading. Runtime power and thermal management has attracted substantial interests in multi-core distributed embedded systems. Fast performance evaluation is an essential step in the research of distributed power and thermal management. In last part of the dissertation, we present an FPGA based emulator of multi-core distributed embedded system designed to support the research in runtime power/thermal management. Hardware and software supports are provided to carry out basic power/thermal management actions including inter-core or inter-FPGA communications, runtime temperature monitoring and dynamic frequency scaling