Predictive Model-Based Thermal Management for Network Applications

As processor power density increases, thermal and power control becomes critical for application processing. In this paper, we consider network applications which feature ON/OFF execution pattern, that causes frequent temperature and power consumption changes in the processor. A novel power aware thermal management algorithm is designed to achieve power saving in multicore processors by employing a vacation scheme. We implement the scheme through the idle states (C-state) provided by the OS in the CPU and show their effectiveness both through analysis and experimental data. Then, we apply our scheme with the thermal constraint and propose a heterogeneous load distribution, which creates more opportunities for power saving. Besides maintaining processor temperature below the temperature constraint, our technique achieves higher sustainable load and better power saving with minimum latency increase compared to existing thermal management techniques. To the best of our knowledge, this is the first work to discuss and develop vacation algorithm considering power, temperature and latency for network application on a general purpose multicore processor.

Temperature estimation and prediction are critical for online regulation of temperature and hot spots on today's high performance processors. In this paper, we present a new method, called FRETEP, to accurately estimate and predict the full-chip temperature at runtime under more practical conditions where we have inaccurate thermal model, less accurate power estimations and limited number of on-chip physical thermal sensors. FRETEP employs a number of new techniques to address this problem. First, we propose a new thermal sensor based error compensation method to correct the errors due to the inaccuracies in thermal model and power estimations. Second, we raise a new correlation based method for error compensation estimation with limited number of thermal sensors. Third, we optimize the compact modeling technique and integrate it into the error compensation process in order to perform the thermal estimation with error compensation at runtime. Last but not least, to enable accurate temperature prediction for the emerging predictive thermal management, we design a full-chip thermal prediction framework employing time series prediction method. Experimental results show FRETEP accurately estimates and predicts the full-chip thermal behavior with very low overhead introduced and compares very favorably with the Kalman filter based approach on standard SPEC benchmarks.

Thermal management of Integration Circuit (IC) becomes more and more important with the scaling of CMOS technology and dramatic increase of power density. Performance of thermal management application is highly related to the accuracy of the temperature monitoring devices. However, due to increasing process variations and parameter drifts, temperature measurements by on-chip thermal sensors may not be accurate unless these sensors are calibrated before shipping the devices to the users. Existing calibration methods impose large time cost and cannot be used for in-field calibration. In this paper, we propose a technique to calibrate on-chip thermal sensors during manufacturing as well as in-field. We model the cyclic dependence between leakage power and temperature and perform calibrations to evaluate the calibration accuracy. Experimental results on 100 dies with different process variation parameters and three corner cases show that our technique have very high accuracy.

Temperature control of the new-generation integrated multicore system is challenging. This is because the leakage power, which is significant in modern systems, is nonlinearly related to temperature, resulting in a complex nonlinear control problem in thermal management. In this article, a new dynamic thermal management (DTM) method with compact piecewise linear (PWL) model based predictive control is proposed to solve the nonlinear control problem. First, a compact PWL thermal model, which takes dynamic power as input, is built by combining multiple local compact linear thermal models expanded at several Taylor expansion points. These local compact linear thermal models are obtained by sampling-based model order reduction with high accuracy. Their Taylor expansion points are selected by a systematic scheme, which exploits the thermal behavior property of the multicore chips. Based on the compact PWL thermal model, a new predictive control method is proposed to compute the future power recommendation for DTM. By approximating the nonlinearity accurately with the compact PWL thermal model and being equipped with predictive control technique, the new DTM achieves an overall high quality temperature management with smooth and accurate temperature tracking. Experimental results show that the new method outperforms the linear model predictive control based method and the echo state network based predictive thermal management method in temperature management quality with lower computing overhead.

Thermal management becomes a prominent issue in system design for both server systems and embedded systems. A system could fail if the peak temperature exceeds its thermal constraint. This research studies thermal-constrained scheduling for frame-based real-time tasks on a dynamic voltage/speed scaling system. Our objective is to design speed schedulers for real-time tasks by utilizing dynamic voltage/speed scaling to meet both timing and thermal constraints. Two approaches are proposed: One is based on the minimization of the response time under the thermal constraint, and the other is based on the minimization of the temperature under the timing constraint. We present detailed schedulability analysis for both proposed approaches. Our data show that our proposed proactive approaches outperform existing reactive ones.

Thermal modeling and heat management of supercapacitor modules for vehicle applications

This paper shows how thermal constraints affect the multidimensional design space for chip multiprocessors, considering the inter-related variables of CPU count, pipeline depth, superscalar width, L2 cache size, and operating voltage and frequency. The results show the importance of thermal modeling and the need for new thermal modeling capabilities and hence the need for collaboration between the thermal engineering and computer architecture communities. Thermal constraints both shift the optimal intra- and inter-core organization, and dominate other physical constraints such as pin-bandwidth and power delivery. Different thermal constraints also require different optimization strategies. For aggressive cooling solutions, reducing power density is at least as important as reducing total power, while for low-cost cooling solutions, reducing total power is more important.

Multi-core processors have become an integral part of mainstream high performance computer systems. In parallel, exponentially increasing power density and packaging costs have necessitated system level thermal management solutions for multi-core systems. Dynamic thermal management (DTM) techniques monitor on-chip temperature continuously and typically employs dynamic voltage and frequency scaling (DVFS) to lower the temperature when it exceeds a pre-defined threshold. State-of-the-art DTM solutions for multi-core systems include distributed DVFS (where each core can scale the voltage/frequency individually) and global DVFS (where all cores scale voltage/frequency simultaneously). Distributed DVFS generally offers higher performance than global DVFS, but it is hard to implement and has major scalability issues.

The stable output of power and the effective control of stack temperature play a very important role in the durability and stability of fuel cell hybrid vehicles. In actual working conditions, energy management of power system and thermal management of Proton Exchange Membrane Fuel Cells (PEMFCs) need to be coordinated with each other to jointly ensure the efficient and stable operation of vehicles, however, most of the research in these two directions is independent. In response to the research gaps mentioned above, the concept of adaptability has been proposed for the first time with the aim of combining these two systems for research. This paper takes the fuel cell hybrid vehicle as the research object and establishes the energy management system and thermal management system model based on Matlab/Simulink software. In order to investigate the adaptability of energy management strategies to the temperature control system, two representative types of rule-based and optimization-based energy management strategies (power-following strategy, PFS, and adaptive equivalent hydrogen consumption minimization strategy, A-EHMS) are designed to be numerically simulated in the driving cycle condition of the UDDS, and then combined with the temperature control system of the fuel cell to comparatively analyze the above two types of energy management strategies from the perspectives of power allocation and the impact on the thermal management system. The results show that the PFS provides better protection for the battery under the same operating conditions. And from the perspective of fuel economy and adaptability to the temperature control system, the fuel consumption of A-EHMS is reduced by 12.5% and the adaptability to the temperature control system is improved by about 13.5% compared to PFS.

A single-phase, non-isothermal model for PEM fuel cells

Thermal management is highly crucial for efficient exploitation of the potentially enormous computational power offered by advanced multi-core processors. Higher temperatures can adversely affect these processors. Without any thermal constraint, a task graph may be scheduled to run on the cores at their maximum voltage. Very often, multiple factors lead to imposing constraints on temperature, ensuring that cores remain below a certain temperature range and yet deliver good performance. The challenge is how to schedule the same task graph under the imposed thermal constraints such that the performance degradation is the minimum. In this paper we present two algorithms for minimizing the performance degradation and the corresponding overhead while satisfying the thermal constraints. The proposed algorithms, named PAVD, and TAVD, adjust a given schedule of a task graph by decreasing the voltage level of judiciously selected tasks in each step. The algorithms differ in the way they select a task at each step and the amount of time spent in searching that task. TAVD selects the tasks by prioritizing among the cores and tasks which attained maximum temperature while PAVD selects the tasks with the minimum performance penalty. For comparison, we develop a simpler greedy-based approach to show that the problem is non-trivial. Extensive experiments using both random and application-oriented task graphs demonstrate that all three algorithms satisfy the imposed thermal constraints by trading-off performance, while each showing its own strength.

Polyethylene glycol modified polysiloxane and silver decorated expanded graphite composites with high thermal conductivity, EMI shielding, and leakage-free performance

In this work, a simulation-based thermal management model for metal parts produced through powder bed fusion (PBF) is proposed. PBF is an additive manufacturing technique that employs a high-energy beam to selectively melt and fuse powder particles layer by layer. The productivity and efficiency of PBF processes can be significantly increased using multi-laser systems with larger build volumes. However, this approach affects the parts thermal history, which can significantly impact their mechanical properties, microstructure, and defects. To address this issue, an algorithm has been developed to calculate adaptive cooling times reaching predefined temperatures at the end of each layer. The algorithm is used in a fast thermal process simulation using layer lumping. The simulation model is applied to a modern multi-laser machine, and the effectiveness of the adaptive cooling times and minimal layer times is evaluated. The results indicate that lower maximum temperatures can be achieved in less manufacturing time with adaptive cooling times than with minimal layer times. However, the significant increase in manufacturing time highlights the need for active cooling systems to utilize multi-laser machines fully. In summary, this paper presents a significant contribution to the field of additive manufacturing, emphasizing the importance of thermal management in ensuring the quality and performance of metal parts produced through PBF.

Thermal management is a critical aspect of engineering applications, playing a pivotal role in enhancing performance and ensuring the reliability of various systems. This paper presents an overview of recent innovations and advancements in thermal management techniques aimed at addressing challenges associated with heat dissipation, thermal regulation, and temperature control in engineering applications. The importance of effective thermal management is highlighted in the context of emerging technologies such as electric vehicles, renewable energy systems, data centers, and electronic devices, where excessive heat generation can lead to performance degradation, component failure, and reduced operational lifespan. The abstract begins by emphasizing the significance of thermal management in engineering applications, emphasizing its role in improving performance and reliability. It discusses the challenges posed by heat generation and the need for innovative solutions to address thermal issues effectively. It hints at the diverse range of techniques to be covered, including passive and active cooling methods, phase change materials, thermal interface materials, and advanced heat exchangers. Furthermore, it suggests that the paper will explore how these techniques contribute to enhanced performance and reliability in engineering applications. In summary, this paper offers insights into the latest developments in thermal management, providing valuable information for researchers, engineers, and industry professionals seeking to optimize thermal performance and ensure the reliability of their systems in diverse engineering applications.

As technology scales down and power density increases, the temperature sensor characteristics will drift, leading to temperature errors which increase over time. Transistor aging is one of the leading contributors to temperature sensing inaccuracies. The prominent aging failure mechanisms like Negative Bias Temperature Instability (NBTI), Hot Carrier Injection (HCI) and electromigration have emerged as the main sources of system unreliability which manifest as an increase in the propagation delay over time. On-chip thermal sensors are not immune to this phenomenon and get affected by these aging mechanisms. Thermal sensor aging exacerbated by increased temperatures leads to temperature sensing inaccuracies requiring repeated sensor calibration. In this work, we propose a novel approach of using performance metrics to predict the transient temperature profile of an application as seen by the aging thermal sensor. Firstly, we make offline profiling of applications and then cluster them into groups using k-means clustering mechanism. Then we use a neural network model to predict the thermal profile of a new application given its performance metrics. The forecasting ability of our model is accessed using MSE and RMSE. This approach is highly scalable and can be used to predict future temperatures which can then be used for run-time dynamic thermal management of multi-core systems.