Design methodology for thermal management using embedded thermoelectric devices

The main objectives of this dissertation is to investigate the prospects of embedded thermoelectric devices integrated in a chip package and to develop a design methodology aimed at taking advantage of the on-chip on-demand cooling capabilities of the thermoelectric devices. First a simulation framework is established and validated against experimental results, which helps to study the cooling capabilities of embedded thermoelectric coolers (TEC) in both a transient and steady state. The potential for up to 15°C of total cooling has been shown. The thermal simulation framework allows for rapid assessment of TEC and system level thermal performance. Next, the thesis develops a co-simulation environment that is capable of simulating the thermal and electrical domain and couples them to design intelligent TEC controllers. These controllers are implemented on chip and can leverage the transient cooling capability of the device. The controllers are simulated within the co-simulation environment and their potential to control high power chip events are thoroughly investigated. The system level overheads are considered and discussions on implementation techniques are presented. The co-simulation framework is also extended to allow for simulation of real predictive technology microprocessor cores and their workloads. Finally the thesis implements a fully on-chip autonomous energy system that takes advantage of the TEC in its reverse energy harvesting mode and uses the same device to harvest energy and use the energy to power the on-chip cooling circuit. This increases the overall energy efficiency of the cooler and verifies the TEC control methods.Ph.D

SUSTAINABLE ENERGY HARVESTING TECHNOLOGIES – PAST, PRESENT AND FUTURE

Chapter 8: Energy Harvesting Technologies: Thick-Film Piezoelectric Microgenerato

Energy challenges for ICT

The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute signi - cantly to the reduction of CO2 emission and enhance resource e ciency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manu- facturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource e - ciency, a multidisciplinary ICT-energy community needs to be brought together cover- ing devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded sys- tems, e cient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging eld and a common framework to strive towards energy-sustainable ICT

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

Energy Harvesting for Structural Health Monitoring Sensor Networks

This report has been developed based on information exchanges at a 2.5-day workshop on energy harvesting for embedded structural health monitoring (SHM) sensing systems that was held June 28-30, 2005, at Los Alamos National Laboratory. The workshop was hosted by the LANL/UCSD Engineering Institute (EI). This Institute is an education- and research-focused collaboration between Los Alamos National Laboratory (LANL) and the University of California, San Diego (UCSD), Jacobs School of Engineering. A Statistical Pattern Recognition paradigm for SHM is first presented and the concept of energy harvesting for embedded sensing systems is addressed with respect to the data acquisition portion of this paradigm. Next, various existing and emerging sensing modalities used for SHM and their respective power requirements are summarized, followed by a discussion of SHM sensor network paradigms, power requirements for these networks and power optimization strategies. Various approaches to energy harvesting and energy storage are discussed and limitations associated with the current technology are addressed. This discussion also addresses current energy harvesting applications and system integration issues. The report concludes by defining some future research directions and possible technology demonstrations that are aimed at transitioning the concept of energy harvesting for embedded SHM sensing systems from laboratory research to field-deployed engineering prototypes

Thermal and QoS-Aware Embedded Systems

While embedded systems such as smartphones and smart cars become essential parts of our lives, they face urgent thermal challenges. Extreme thermal conditions (i.e., both high and low temperatures) degrade system reliability, even risking safety; devices in the cold environments unexpectedly go offline, whereas extremely high device temperatures can cause device failures or battery explosions. These thermal limits become close to the norm because of ever-increasing chip power densities and application complexities. Embedded systems in the wild, however, lack adaptive and effective solutions to overcome such thermal challenges. An adaptive thermal management solution must cope with various runtime thermal scenarios under a changing ambient temperature. An effective solution requires the understanding of the dynamic thermal behaviors of underlying hardware and application workloads to ensure thermal and application quality-of-service (QoS) requirements. This thesis proposes a suite of adaptive and effective thermal management solutions to address different aspects of real-world thermal challenges faced by modern embedded systems. First, we present BPM, a battery-aware power management framework for mobile devices to address the unexpected device shutoffs in cold environments. We develop BPM as a background service that characterizes and controls real-time battery behaviors to maintain operable conditions even in cold environments. We then propose eTEC, building on the thermoelectric cooling solution, which adaptively controls cooling and computational power to avoid mobile devices overheating. For the real-time embedded systems such as cars, we present RT-TRM, a thermal-aware resource management framework that monitors changing ambient temperatures and allocates system resources to individual tasks. Next, we target in-vehicle vision systems running on CPUs–GPU system-on-chips and develop CPU–GPU co-scheduling to tackle thermal imbalance across CPUs caused by GPU heat. We evaluate all of these solutions using representative mobile/automotive platforms and workloads, demonstrating their effectiveness in meeting thermal and QoS requirements.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/153350/1/ymoonlee_1.pd

Energy Neutral Design of Embedded Systems for Resource Constrained Monitoring Applications

Automatic monitoring of environments, resouces and human processes are crucial and foundamental tasks to improve people's quality of life and to safeguard the natural environment. Today, new technologies give us the possibility to shape a greener and safer future. The more specialized is the kind of monitoring we want to achieve, more tight are the constraints in terms of reliability, low energy and maintenance-free autonomy. The challenge in case of tight energy constraints is to find new techniques to save as much power as possible or to retrieve it from the very same environment where the system operates, towards the realization of energy neutral embedded monitoring systems. Energy efficiency and battery autonomy of such devices are still the major problem impacting reliability and penetration of such systems in risk-related activities of our daily life. Energy management must not be optimized to the detriment of the quality of monitoring and sensors can not be operated without supply. In this thesis, I present different embedded system designs to bridge this gap, both from the hardware and software sides, considering specific resource constrained scenarios as case studies that have been used to develop solutions with much broader validity. Results achieved demonstrate that energy neutrality in monitoring under resource constrained conditions can be obtained without compromising efficiency and reliability of the outcomes

Energy challenges for ICT

The energy consumption from the expanding use of information and communications technology (ICT) is unsustainable with present drivers, and it will impact heavily on the future climate change. However, ICT devices have the potential to contribute significantly to the reduction of CO2 emission and enhance resource efficiency in other sectors, e.g., transportation (through intelligent transportation and advanced driver assistance systems and self-driving vehicles), heating (through smart building control), and manufacturing (through digital automation based on smart autonomous sensors). To address the energy sustainability of ICT and capture the full potential of ICT in resource efficiency, a multidisciplinary ICT-energy community needs to be brought together covering devices, microarchitectures, ultra large-scale integration (ULSI), high-performance computing (HPC), energy harvesting, energy storage, system design, embedded systems, efficient electronics, static analysis, and computation. In this chapter, we introduce challenges and opportunities in this emerging field and a common framework to strive towards energy-sustainable ICT

ENERGY-AWARE OPTIMIZATION FOR EMBEDDED SYSTEMS WITH CHIP MULTIPROCESSOR AND PHASE-CHANGE MEMORY

Over the last two decades, functions of the embedded systems have evolved from simple real-time control and monitoring to more complicated services. Embedded systems equipped with powerful chips can provide the performance that computationally demanding information processing applications need. However, due to the power issue, the easy way to gain increasing performance by scaling up chip frequencies is no longer feasible. Recently, low-power architecture designs have been the main trend in embedded system designs. In this dissertation, we present our approaches to attack the energy-related issues in embedded system designs, such as thermal issues in the 3D chip multiprocessor (CMP), the endurance issue in the phase-change memory(PCM), the battery issue in the embedded system designs, the impact of inaccurate information in embedded system, and the cloud computing to move the workload to remote cloud computing facilities. We propose a real-time constrained task scheduling method to reduce peak temperature on a 3D CMP, including an online 3D CMP temperature prediction model and a set of algorithm for scheduling tasks to different cores in order to minimize the peak temperature on chip. To address the challenging issues in applying PCM in embedded systems, we propose a PCM main memory optimization mechanism through the utilization of the scratch pad memory (SPM). Furthermore, we propose an MLC/SLC configuration optimization algorithm to enhance the efficiency of the hybrid DRAM + PCM memory. We also propose an energy-aware task scheduling algorithm for parallel computing in mobile systems powered by batteries. When scheduling tasks in embedded systems, we make the scheduling decisions based on information, such as estimated execution time of tasks. Therefore, we design an evaluation method for impacts of inaccurate information on the resource allocation in embedded systems. Finally, in order to move workload from embedded systems to remote cloud computing facility, we present a resource optimization mechanism in heterogeneous federated multi-cloud systems. And we also propose two online dynamic algorithms for resource allocation and task scheduling. We consider the resource contention in the task scheduling

Advanced Energy Harvesting Technologies

Energy harvesting is the conversion of unused or wasted energy in the ambient environment into useful electrical energy. It can be used to power small electronic systems such as wireless sensors and is beginning to enable the widespread and maintenance-free deployment of Internet of Things (IoT) technology. This Special Issue is a collection of the latest developments in both fundamental research and system-level integration. This Special Issue features two review papers, covering two of the hottest research topics in the area of energy harvesting: 3D-printed energy harvesting and triboelectric nanogenerators (TENGs). These papers provide a comprehensive survey of their respective research area, highlight the advantages of the technologies and point out challenges in future development. They are must-read papers for those who are active in these areas. This Special Issue also includes ten research papers covering a wide range of energy-harvesting techniques, including electromagnetic and piezoelectric wideband vibration, wind, current-carrying conductors, thermoelectric and solar energy harvesting, etc. Not only are the foundations of these novel energy-harvesting techniques investigated, but the numerical models, power-conditioning circuitry and real-world applications of these novel energy harvesting techniques are also presented