abstract: Many core modern multiprocessor systems-on-chip offers tremendous power and performance optimization opportunities by tuning thousands of potential voltage, frequency and core configurations. Applications running on these architectures are becoming increasingly complex. As the basic building blocks, which make up the application, change during runtime, different configurations may become optimal with respect to power, performance or other metrics. Identifying the optimal configuration at runtime is a daunting task due to a large number of workloads and configurations. Therefore, there is a strong need to evaluate the metrics of interest as a function of the supported configurations. This thesis focuses on two different types of modern multiprocessor systems-on-chip (SoC): Mobile heterogeneous systems and tile based Intel Xeon Phi architecture. For mobile heterogeneous systems, this thesis presents a novel methodology that can accurately instrument different types of applications with specific performance monitoring calls. These calls provide a rich set of performance statistics at a basic block level while the application runs on the target platform. The target architecture used for this work (Odroid XU3) is capable of running at 4940 different frequency and core combinations. With the help of instrumented application vast amount of characterization data is collected that provides details about performance, power and CPU state at every instrumented basic block across 19 different types of applications. The vast amount of data collected has enabled two runtime schemes. The first work provides a methodology to find optimal configurations in heterogeneous architecture using classifiers and demonstrates an average increase of 93%, 81% and 6% in performance per watt compared to the interactive, ondemand and powersave governors, respectively. The second work using same data shows a novel imitation learning framework for dynamically controlling the type, number, and the frequencies of active cores to achieve an average of 109% PPW improvement compared to the default governors. This work also presents how to accurately profile tile based Intel Xeon Phi architecture while training different types of neural networks using open image dataset on deep learning framework. The data collected allows deep exploratory analysis. It also showcases how different hardware parameters affect performance of Xeon Phi. / Dissertation/Thesis / Masters Thesis Engineering 2019

<div>With the upsurge of greenhouse gas emissions and rapid depletion of fossil fuels, the pressure on the transportation industry to develop new vehicles with improved fuel economy without sacrificing performance is on the rise. Hybrid Electric Vehicles (HEVs), which employ an internal combustion engine as well as an electric motor as power sources, are becoming increasingly popular alternatives to traditional engine only vehicles. However, the presence of multiple power sources makes HEVs more complex. A significant task in developing an HEV is designing a power management strategy, defined as a control system tasked with the responsibility of efficiently splitting the power/torque demand from the separate energy sources. Five different types of power management strategies, which were developed previously, are reviewed in this work, including dynamic programming, equivalent consumption minimization strategy, proportional state-of-charge algorithm, regression modeling and long short term memory modeling. The effects of these power management strategies on the vehicle performance are studied using a simplified model of the vehicle. This work also proposes an original power management strategy development using a genetic algorithm. This power management strategy is compared to dynamic programming and several similarities and differences are observed in the results of dynamic programming and genetic algorithm. For a particular drive cycle, the implementation of the genetic algorithm strategy on the vehicle model leads to a vehicle speed profile that almost matches the original speed profile of that drive cycle.</div>

Environmental awareness, production costs and operating expenses have provided a large incentive for the investigation of novel and more efficient fluid power technologies for decades. In the earth-moving sector, hydraulic hybrids have emerged as a highly efficient and affordable choice for the next generation hydraulic systems. Displacementcontrolled (DC) actuation has demonstrated that, when coupled with hydraulic hybrids, the engine power can be downsized by up to 50% leading to substantial savings. This concept has been realized by the authors‘ group on an excavator prototype where a secondary-controlled hydraulic hybrid drive was implemented on the swing. Actuatorlevel controls have been formulated by the authors‘ group but the challenge remains to effectively manage the system on the supervisory-level. In this paper, a power management controller is proposed to minimize fuel consumption while taking into account performance. The algorithm, a feedforward and cost-function combination considers operator commands, the DC actuators‘ power consumption and the power available from the engine and hydraulic hybrid as metrics. The developed strategy brings the technology closer to the predicted savings while achieving superior operability.

IT equipment has been estimated to be responsible for 2% of global CO2 emissions and data centers are responsible for 1.2% of U.S. energy consumption. With the large quantity of high quality digital content available on the Internet the energy demands and environmental impact of the data centers must be addressed. The use of peer-to-peer technologies, such as BitTorrent, to distribute legal content to consumers is actively being explored as a means of reducing both file download times and the energy consumption of data centers. This approach pushes the energy use out of the data centers and into the homes of content consumers (who are also then content distributors). The current BitTorrent protocol requires that clients must be fully powered-on to be participating members in a swarm. In this thesis, an extension to the BitTorrent protocol that utilizes long-lived knowledge of sleeping peers to enable clients to sleep when not actively distributing content yet remain responsive swarm members is developed. New peer states and events required for the protocol extension, the implementation the new protocol in a simulation environment, and the implementation of the protocol extension in a real client are described. Experiments on a simulated swarm of 51 peers transferring a 1 GB and a real swarm of 11 peers transfer- ring a 100 MB file were run. To validate the simulation a simulated swarm of 11 peers transferring a 100 MB file is compared to the real swarm of 11 peers. The results of standard BitTorrent are compared to the new Green BitTorrent by examining download times, sleep time, and awake time. The results of the experiment show significant energy savings are possible with only a small penalty in download time. Energy savings of up to 75% are shown with download time increases as little as 10%. These energy savings could equate to over $1 billion dollars per year in the US alone if Green BitTorrent is used instead of standard BitTorrent for future rollouts of legal distribution systems.

Indiana University-Purdue University Indianapolis (IUPUI) / An increase in the awareness of environmental conservation is leading the automotive industry into the adaptation of alternatively fueled vehicles. Electric, Fuel-Cell as well as Hybrid-Electric vehicles focus on this research area with the aim to efficiently utilize vehicle powertrain as the first step. Energy and Power Management System control strategies play a vital role in improving the efficiency of any hybrid propulsion system. However, these control strategies are sensitive to the dynamics of the powertrain components used in the given system. A kinematic mathematical model for Plug-in Hybrid Electric Vehicle (PHEV) has been developed in this study and is further optimized by determining optimal power management strategy for minimal fuel consumption as well as NOx emissions while executing a set drive cycle. A multi-objective optimization using weighted sum formulation is needed in order to observe the trade-off between the optimized objectives. Particle Swarm Optimization (PSO) algorithm has been used in this research, to determine the trade-off curve between fuel and NOx. In performing these optimizations, the control signal consisting of engine speed and reference battery SOC trajectory for a 2-hour cycle is used as the controllable decision parameter input directly from the optimizer. Each element of the control signal was split into 50 distinct points representing the full 2 hours, giving slightly less than 2.5 minutes per point, noting that the values used in the model are interpolated between the points for each time step. With the control signal consisting of 2 distinct signals, speed, and SOC trajectory, as 50 element time-variant signals, a multidimensional problem was formulated for the optimizer. Novel approaches to balance the optimizer exploration and convergence, as well as seeding techniques are suggested to solve the optimal control problem. The optimization of each involved individual runs at 5 different weight levels with the resulting cost populations being compiled together to visually represent with the help of Pareto front development. The obtained results of simulations and optimization are presented involving performances of individual components of the PHEV powertrain as well as the optimized PMS strategy to follow for a given drive cycle. Observations of the trade-off are discussed in the case of Multi-Objective Optimizations.

abstract: Power management circuits are employed in most electronic integrated systems, including applications for automotive, IoT, and smart wearables. Oftentimes, these power management circuits become a single point of system failure, and since they are present in most modern electronic devices, they become a target for hardware security attacks. Digital circuits are typically more prone to security attacks compared to analog circuits, but malfunctions in digital circuitry can affect the analog performance/parameters of power management circuits. This research studies the effect that these hacks will have on the analog performance of power circuits, specifically linear and switching power regulators/converters. Apart from security attacks, these circuits suffer from performance degradations due to temperature, aging, and load stress. Power management circuits usually consist of regulators or converters that regulate the load’s voltage supply by employing a feedback loop, and the stability of the feedback loop is a critical parameter in the system design. Oftentimes, the passive components employed in these circuits shift in value over varying conditions and may cause instability within the power converter. Therefore, variations in the passive components, as well as malicious hardware security attacks, can degrade regulator performance and affect the system’s stability. The traditional ways of detecting phase margin, which indicates system stability, employ techniques that require the converter to be in open loop, and hence can’t be used while the system is deployed in-the-field under normal operation. Aging of components and security attacks may occur after the power management systems have completed post-production test and have been deployed, and they may not cause catastrophic failure of the system, hence making them difficult to detect. These two issues of component variations and security attacks can be detected during normal operation over the product lifetime, if the frequency response of the power converter can be monitored in-situ and in-field. This work presents a method to monitor the phase margin (stability) of a power converter without affecting its normal mode of operation by injecting a white noise/ pseudo random binary sequence (PRBS). Furthermore, this work investigates the analog performance parameters, including phase margin, that are affected by various digital hacks on the control circuitry associated with power converters. A case study of potential hardware attacks is completed for a linear low-dropout regulator (LDO). / Dissertation/Thesis / Masters Thesis Electrical Engineering 2019

A conceptual dust detection mission, KnightSat III, using pico-scale satellites is analyzed. The purpose of the proposed KnightSat III mission is to aid in the determination of the size, mass, distribution, and number of dust particles in low earth orbits through a low cost and flexible satellite or a formation of satellites equipped with a new dust detector. The analysis of a single satellite mission with an on-board dust detector is described; though this analysis can easily be extended to a formation of pico-scale satellites. Many design aspects of the mission are discussed, including orbit analysis, power management, attitude determination and control, and mass and power budgets. Two of them are emphasized. The first is a new attitude guidance and control method, and the second is the online optimal power scheduling. It is expected that the measurements obtained from this possible future mission will provide insight into the dynamical processes of inner solar system dust, as well as aid in designing proper micro-meteoroid impact mitigation strategies for future man-made spacecraft.

Edge networks such as sensor, mobile, and disruption tolerant networks suffer from topological uncertainty and disconnections due to myriad of factors including limited battery capacity on client devices and mobility. Hence, providing reliable, always-on consistency for network applications in such mobile and sensor systems is non-trivial and challenging. However, the problem is of paramount importance given the proliferation of mobile phones, PDAs, laptops, and music players. This thesis identifies two fundamental deterrents to addressing the above problem. First, limited energy on client mobile and sensor devices makes high levels of consistency and availability impossible. Second, unreliable support from the network infrastructure, such as coverage holes in WiFi degrades network performance. We address these two issues in this dissertation through client and infrastructure end modifications. The first part of this thesis proposes a novel energy management architecture called Hierarchical Power Management (HPM). HPM combines platforms with diverse energy needs and capabilities into a single integrated system to provide high levels of consistency and availability at minimal energy consumption. We present two systems Triage and Turducken which are instantiations of HPM for sensor net microservers and laptops respectively. The second part of the thesis proposes and analyzes the use of additional infrastructure in the form of relays, mesh nodes, and base stations to enhance sparse and dense mobile networks. We present the design, implementation, and deployment of Throwboxes a relay system to enhance sparse mobile networks and an associated system for enhancing WiFi based mobile networks.

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