Modeling the Temperature Bias of Power Consumption for Nanometer-Scale CPUs in Application Processors

We introduce and experimentally validate a new macro-level model of the CPU temperature/power relationship within nanometer-scale application processors or system-on-chips. By adopting a holistic view, this model is able to take into account many of the physical effects that occur within such systems. Together with two algorithms described in the paper, our results can be used, for instance by engineers designing power or thermal management units, to cancel the temperature-induced bias on power measurements. This will help them gather temperature-neutral power data while running multiple instance of their benchmarks. Also power requirements and system failure rates can be decreased by controlling the CPU's thermal behavior. Even though it is usually assumed that the temperature/power relationship is exponentially related, there is however a lack of publicly available physical temperature/power measurements to back up this assumption, something our paper corrects. Via measurements on two pertinent platforms sporting nanometer-scale application processors, we show that the power/temperature relationship is indeed very likely exponential over a 20{\deg}C to 85{\deg}C temperature range. Our data suggest that, for application processors operating between 20{\deg}C and 50{\deg}C, a quadratic model is still accurate and a linear approximation is acceptable.Comment: Submitted to SAMOS 2014; International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIV

DeadPool: Performance Deadline Based Frequency Pooling and Thermal Management Agent in DVFS Enabled MPSoCs

High operating temperature and frequent thermal cycles in a multi-processor system-on-chip, which is now popularly utilized in mobile/Edge devices, harm the overall lifespan and reliability of such devices. In this paper, we propose an intelligent software agent that works alongside other resource mapping and partitioning mechanism in order to monitor and reduce the operating temperature of the system by regulating the operating frequency of the CPU cores while catering for performance constraint at the same time. Our proposed approach?, DeadPool thermal management agent, is able to reduce the overall operating temperature of the system by 24.21% and reduce thermal cycle by 67.42% at the most when compared to the state-of-the-art methods

Techniques for Aging, Soft Errors and Temperature to Increase the Reliability of Embedded On-Chip Systems

This thesis investigates the challenge of providing an abstracted, yet sufficiently accurate reliability estimation for embedded on-chip systems. In addition, it also proposes new techniques to increase the reliability of register files within processors against aging effects and soft errors. It also introduces a novel thermal measurement setup that perspicuously captures the infrared images of modern multi-core processors

Improving processor efficiency through thermal modeling and runtime management of hybrid cooling strategies

One of the main challenges in building future high performance systems is the ability to maintain safe on-chip temperatures in presence of high power densities. Handling such high power densities necessitates novel cooling solutions that are significantly more efficient than their existing counterparts. A number of advanced cooling methods have been proposed to address the temperature problem in processors. However, tradeoffs exist between performance, cost, and efficiency of those cooling methods, and these tradeoffs depend on the target system properties. Hence, a single cooling solution satisfying optimum conditions for any arbitrary system does not exist. This thesis claims that in order to reach exascale computing, a dramatic improvement in energy efficiency is needed, and achieving this improvement requires a temperature-centric co-design of the cooling and computing subsystems. Such co-design requires detailed system-level thermal modeling, design-time optimization, and runtime management techniques that are aware of the underlying processor architecture and application requirements. To this end, this thesis first proposes compact thermal modeling methods to characterize the complex thermal behavior of cutting-edge cooling solutions, mainly Phase Change Material (PCM)-based cooling, liquid cooling, and thermoelectric cooling (TEC), as well as hybrid designs involving a combination of these. The proposed models are modular and they enable fast and accurate exploration of a large design space. Comparisons against multi-physics simulations and measurements on testbeds validate the accuracy of our models (resulting in less than 1C error on average) and demonstrate significant reductions in simulation time (up to four orders of magnitude shorter simulation times). This thesis then introduces temperature-aware optimization techniques to maximize energy efficiency of a given system as a whole (including computing and cooling energy). The proposed optimization techniques approach the temperature problem from various angles, tackling major sources of inefficiency. One important angle is to understand the application power and performance characteristics and to design management techniques to match them. For workloads that require short bursts of intense parallel computation, we propose using PCM-based cooling in cooperation with a novel Adaptive Sprinting technique. By tracking the PCM state and incorporating this information during runtime decisions, Adaptive Sprinting utilizes the PCM heat storage capability more efficiently, achieving 29\% performance improvement compared to existing sprinting policies. In addition to the application characteristics, high heterogeneity in on-chip heat distribution is an important factor affecting efficiency. Hot spots occur on different locations of the chip with varying intensities; thus, designing a uniform cooling solution to handle worst-case hot spots significantly reduces the cooling efficiency. The hybrid cooling techniques proposed as part of this thesis address this issue by combining the strengths of different cooling methods and localizing the cooling effort over hot spots. Specifically, the thesis introduces LoCool, a cooling system optimizer that minimizes cooling power under temperature constraints for hybrid-cooled systems using TECs and liquid cooling. Finally, the scope of this work is not limited to existing advanced cooling solutions, but it also extends to emerging technologies and their potential benefits and tradeoffs. One such technology is integrated flow cell array, where fuel cells are pumped through microchannels, providing both cooling and on-chip power generation. This thesis explores a broad range of design parameters including maximum chip temperature, leakage power, and generated power for flow cell arrays in order to maximize the benefits of integrating this technology with computing systems. Through thermal modeling and runtime management techniques, and by exploring the design space of emerging cooling solutions, this thesis provides significant improvements in processor energy efficiency.2018-07-09T00:00:00

Modeling DVFS and Power-Gating Actuators for Cycle-Accurate NoC-Based Simulators

Networks-on-chip (NoCs) are a widely recognized viable interconnection paradigm to support the multi-core revolution. One of the major design issues of multicore architectures is still the power, which can no longer be considered mainly due to the cores, since the NoC contribution to the overall energy budget is relevant. To face both static and dynamic power while balancing NoC performance, different actuators have been exploited in literature, mainly dynamic voltage frequency scaling (DVFS) and power gating. Typically, simulation-based tools are employed to explore the huge design space by adopting simplified models of the components. As a consequence, the majority of state-of-the-art on NoC power-performance optimization do not accurately consider timing and power overheads of actuators, or (even worse) do not consider them at all, with the risk of overestimating the benefits of the proposed methodologies. This article presents a simulation framework for power-performance analysis of multicore architectures with specific focus on the NoC. It integrates accurate power gating and DVFS models encompassing also their timing and power overheads. The value added of our proposal is manyfold: (i) DVFS and power gating actuators are modeled starting from SPICE-level simulations; (ii) such models have been integrated in the simulation environment; (iii) policy analysis support is plugged into the framework to enable assessment of different policies; (iv) a flexible GALS (globally asynchronous locally synchronous) support is provided, covering both handshake and FIFO re-synchronization schemas. To demonstrate both the flexibility and extensibility of our proposal, two simple policies exploiting the modeled actuators are discussed in the article