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

Thermoelectric Energy Conversion: Materials, Devices, and Systems

This paper will present a discussion of challenges, progresses, and opportunities in thermoelectric energy conversion technology. We will start with an introduction to thermoelectric technology, followed by discussing advances in thermoelectric materials, devices, and systems. Thermoelectric energy conversion exploits the Seebeck effect to convert thermal energy into electricity, or the Peltier effect for heat pumping applications. Thermoelectric devices are scalable, capable of generating power from nano Watts to mega Watts. One key issue is to improve materials thermoelectric figure- of-merit that is linearly proportional to the Seebeck coefficient, the square of the electrical conductivity, and inversely proportional to the thermal conductivity. Improving the figure-of-merit requires good understanding of electron and phonon transport as their properties are often contradictory in trends. Over the past decade, excellent progresses have been made in the understanding of electron and phonon transport in thermoelectric materials, and in improving existing and identify new materials, especially by exploring nanoscale size effects. Taking materials to real world applications, however, faces more challenges in terms of materials stability, device fabrication, thermal management and system design. Progresses and lessons learnt from our effort in fabricating thermoelectric devices will be discussed. We have demonstrated device thermal-to-electrical energy conversion efficiency ~10% and solar-thermoelectric generator efficiency at 4.6% without optical concentration of sunlight (Figure 1) and ~8-9% efficiency with optical concentration. Great opportunities exist in advancing materials as well as in using existing materials for energy efficiency improvements and renewable energy utilization, as well as mobile applications

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

Relay based thermal aware and mobility support routing protocol for wireless body sensor networks

The evolvement of wireless technologies has enabled revolutionizing the health-care industry by monitor patient health condition requiring early diagnosis and interfering when a chronic situation is taking place. In this regard, miniaturized biosensors have been manufactured to cover various medical applications forming therefore a Wireless Body Sensor Network (WBSN). A WBSN is comprised of several small and low power devices capable of sensing vital signs such as heart rate, blood glucose, body temperature etc.. Although WBSN main purpose is to provide the most convenient wireless setting for the networking of human body sensors, there are still a great number of technical challenges to resolve such as: power source miniaturization, low power transceivers, biocompatibility, secure data transfer, minimum transmission delay and high quality of service. These challenges have to be taken into consideration when creating a new routing protocol for WBSNs. This paper proposes a new Relay based Thermal aware and Mobile Routing Protocol (RTM-RP) for Wireless Body Sensor Networks tackling the problem of high energy consumption and high temperature increase where the mobility is a crucial constraint to handle

Novel DVFS Methodologies For Power-Efficient Mobile MPSoC

Low power mobile computing systems such as smartphones and wearables have become an integral part of our daily lives and are used in various ways to enhance our daily lives. Majority of modern mobile computing systems are powered by multi-processor System-on-a-Chip (MPSoC), where multiple processing elements are utilized on a single chip. Given the fact that these devices are battery operated most of the times, thus, have limited power supply and the key challenges include catering for performance while reducing the power consumption. Moreover, the reliability in terms of lifespan of these devices are also affected by the peak thermal behaviour on the device, which retrospectively also make such devices vulnerable to temperature side-channel attack. This thesis is concerned with performing Dynamic Voltage and Frequency Scaling (DVFS) on different processing elements such as CPU & GPU, and memory unit such as RAM to address the aforementioned challenges. Firstly, we design a Computer Vision based machine learning technique to classify applications automatically into different categories of workload such that DVFS could be performed on the CPU to reduce the power consumption of the device while executing the application. Secondly, we develop a reinforcement learning based agent to perform DVFS on CPU and GPU while considering the user's interaction with such devices to optimize power consumption and thermal behaviour. Next, we develop a heuristic based automated agent to perform DVFS on CPU, GPU and RAM to optimize the same while executing an application. Finally, we explored the affect of DVFS on CPUs leading to vulnerabilities against temperature side-channel attack and hence, we also designed a methodology to secure against such attack while improving the reliability in terms of lifespan of such devices