abstract: User satisfaction is pivotal to the success of mobile applications. At the same time, it is imperative to maximize the energy efficiency of the mobile device to ensure optimal usage of the limited energy source available to mobile devices while maintaining the necessary levels of user satisfaction. However, this is complicated due to user interactions, numerous shared resources, and network conditions that produce substantial uncertainty to the mobile device's performance and power characteristics. In this dissertation, a new approach is presented to characterize and control mobile devices that accurately models these uncertainties. The proposed modeling framework is a completely data-driven approach to predicting power and performance. The approach makes no assumptions on the distributions of the underlying sources of uncertainty and is capable of predicting power and performance with over 93% accuracy. Using this data-driven prediction framework, a closed-loop solution to the DEM problem is derived to maximize the energy efficiency of the mobile device subject to various thermal, reliability and deadline constraints. The design of the controller imposes minimal operational overhead and is able to tune the performance and power prediction models to changing system conditions. The proposed controller is implemented on a real mobile platform, the Google Pixel smartphone, and demonstrates a 19% improvement in energy efficiency over the standard frequency governor implemented on all Android devices. / Dissertation/Thesis / Doctoral Dissertation Computer Engineering 2017

abstract: Nearly 60% of the world population uses a mobile phone, which is typically powered by a system-on-chip (SoC). While the mobile platform capabilities range widely, responsiveness, long battery life and reliability are common design concerns that are crucial to remain competitive. Consequently, state-of-the-art mobile platforms have become highly heterogeneous by combining a powerful SoC with numerous other resources, including display, memory, power management IC, battery and wireless modems. Furthermore, the SoC itself is a heterogeneous resource that integrates many processing elements, such as CPU cores, GPU, video, image, and audio processors. Therefore, CPU cores do not dominate the platform power consumption under many application scenarios. Competitive performance requires higher operating frequency, and leads to larger power consumption. In turn, power consumption increases the junction and skin temperatures, which have adverse effects on the device reliability and user experience. As a result, allocating the power budget among the major platform resources and temperature control have become fundamental consideration for mobile platforms. Dynamic thermal and power management algorithms address this problem by putting a subset of the processing elements or shared resources to sleep states, or throttling their frequencies. However, an adhoc approach could easily cripple the performance, if it slows down the performance-critical processing element. Furthermore, mobile platforms run a wide range of applications with time varying workload characteristics, unlike early generations, which supported only limited functionality. As a result, there is a need for adaptive power and performance management approaches that consider the platform as a whole, rather than focusing on a subset. Towards this need, our specific contributions include (a) a framework to dynamically select the Pareto-optimal frequency and active cores for the heterogeneous CPUs, such as ARM big.Little architecture, (b) a dynamic power budgeting approach for allocating optimal power consumption to the CPU and GPU using performance sensitivity models for each PE, (c) an adaptive GPU frame time sensitivity prediction model to aid power management algorithms, and (d) an online learning algorithm that constructs adaptive run-time models for non-stationary workloads. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2018

Nowadays, thanks to the continuous improvements in the integration scale, more and more cores are added on the same chip, leading to higher system performance. In order to interconnect all nodes, a network-on-chip (NoC) is used, which is in charge of delivering data between cores. However, increasing the number of cores leads to a significant power consumption increase, leading the NoC to be one of the most expensive components in terms of power. Because of this, during the last years, several mechanisms have been proposed to address the NoC power consumption by means of DVFS (Dynamic Voltage and Frequency Scaling) and power-gating strategies. Nevertheless, improvements achieved by these mechanisms are achieved, to a greater or lesser extent, at the cost of system performance, potentially increasing the risk of saturating the network by forming congested points which, in turn, compromise the rest of the system functionality. One side effect is the creation of the "Head-of-Line blocking" effect where congested packets at the head of queues prevent other non-blocked packets from advancing. To address this issue, in this thesis, on one hand, we propose novel congestion control techniques in order to improve system performance by removing the "Head-of-Line" blocking effect. On the other hand, we propose combined solutions adapted to DVFS in order to achieve improvements in terms of performance and power. In addition to this, we propose a path-aware power-gating-based mechanism, which is capable of detecting the flows sharing buffer resources along data paths and perform to switch them off when not needed. With all these combined solutions we can significantly reduce the power consumption of the NoC when compared with state-of-the-art proposals. / Hoy en día, gracias a las mejoras en la escala de integración cada vez se integran más y más núcleos en un mismo chip, mejorando así sus prestaciones. Para interconectar todos los nodos dentro del chip se emplea una red en chip (NoC, Network-on-Chip), la cual es la encargada de intercambiar información entre núcleos. No obstante, aumentar el número de núcleos en el chip también conlleva a su vez un importante incremento en el consumo de la NoC, haciendo que ésta se convierta en una de las partes más caras del chip en términos de consumo. Por ello, en los últimos años se han propuesto diversas técnicas de ahorro de energía orientadas a reducir el consumo de la NoC mediante el uso de DVFS (Dynamic Voltage and Frequency Scaling) o estrategias basadas en "power-gating". Sin embargo, éstas mejoras de consumo normalmente se obtienen a costa de sacrificar, en mayor o menor medida, las prestaciones del sistema, aumentado potencialmente así el riesgo de saturar la red, generando puntos de congestión que, a su vez, comprometen el rendimiento del resto del sistema. Un efecto colateral es el "Head-of-Line blocking", mediante el que paquetes congestionados en la cabeza de la cola impiden que otros paquetes no congestionados avancen. Con el fin de solucionar este problema, en ésta tesis, en primer lugar, proponemos técnicas novedosas de control de congestión para incrementar el rendimiento del sistema mediante la eliminación del "Head-of-Line blocking", mientras que, por otra parte, proponemos soluciones combinadas adaptadas a DVFS con el fin de conseguir mejoras en términos de rendimiento y energía. Además, proponemos una técnica de "power-gating" orientada a rutas de datos, la cual es capaz de detectar flujos de datos compartiendo recursos a lo largo de rutas y apagar dichos recursos de forma dinámica cuando no son necesarios. Con todas éstas soluciones combinadas podemos reducir el consumo de energía de la NoC en comparación con otras técnicas presentes en el estado del arte. / Hui en dia, gr\`acies a les millores en l'escala d'integraci\'o, cada vegada s'integren m\'es i m\'es nuclis en un mateix xip, la qual cosa millora les seues prestacions. Per tal d'interconectar tots els nodes dins el xip es fa \'us d'una Xarxa en Xip (NoC; Network-on-Chip), la qual \'es l'encarregada d'intercanviar informaci\'o entre els nuclis. No obstant aix\`o, incrementar el nombre de nuclis en el xip tamb\'e comporta un important augment en el consum de la NoC, la qual cosa fa que aquesta es convertisca en una de les parts m\'es costoses del xip en termes de consum. Per aix\`o, en els \'ultims anys s'han proposat diverses t\`ecniques d'estalvi d'energia orientades a reduir el consum de la NoC mitjançant l'\'us de DVFS (Dynamic Voltage and Frequency Scaling) o estrat\`egies basades en ``power-gating''. Malgrat aix\`o, aquestes millores en les prestacions normalment s'obtenen a costa de sacrificar, en major o menor mesura, les prestacions del sistema i augmenta aix\'i el risc de saturar la xarxa al generar-se punts de congesti\'o, que al mateix temps, comprometen el rendiment de la resta del sistema. Un efecte col-lateral \'es el ``Head-of- Line blocking'', mitjançant el qual, els paquets congestionats al cap de la cua, impedixen que altres paquets no congestionats avancen. A fi de solucionar eixe problema, en aquesta tesi, en primer lloc, proposem noves t\`ecniques de control de congesti\'o amb l'objectiu d'incrementar el rendiment del sistema per mitj\`a de l'eliminaci\'o del ``Head-of- Line blocking'', i d'altra banda, proposem solucions combinades adaptades a DVFS amb la finalitat d'aconseguir millores en termes de rendiment i energia. A m\'es, proposem una t\`ecnica de ``power-gating'' orientada a rutes de dades, la qual \'es capa\c c de detectar fluxos de dades al compartir recursos al llarg de les rutes i apagar eixos recursos de forma din\`amica quan no s\'on necessaris. Amb totes aquestes solucions combinades podem reduir el consum d'energia de la NoC en comparaci\'o amb altres t\`ecniques presents en l'estat de l'art. / Escamilla López, JV. (2017). Head-of-Line Blocking Reduction in Power-Efficient Networks-on-Chip [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90419 / TESIS

The continued scaling of transistors has led to an exponential increase in on-chip power density, which has resulted in increasing temperature. In turn, the increase in temperature directly leads to the increase in the rate of wear of a processor. Negative-bias temperature instability (NBTI) is one of the most dominant integrated circuit (IC) failure mechanisms [13, 5] that strongly depends on temperature. NBTI manifests in the form of increased circuit delays which can lead to timing failures and processor crashes. The ability to monitor the wear progression of a processor due to NBTI is valuable when designing real-time embedded systems. While NBTI can be detected using wear state sensors, not all chips are equipped with these sensors because detecting wear due to NBTI requires modifications to the chip design and incurs area and power overhead. NBTI sensor data may also not be exposed to users in software. In addition, wear sensors cannot take into account variations in wear due to the differences in the wear sensor devices and the other functional devices and their operating conditions. In this paper, we propose a lightweight, online methodology to monitor the wear process due to NBTI for off-the-shelf embedded processors. Our proposed method requires neither data on the threshold voltage and critical paths nor additional hardware. Our methodology can also be extended to predict the wear progression due to some other dominant IC failure mechanisms. Experiments on embedded processors provide insights on NBTI wear progression over time. This knowledge can be used to design real-time embedded systems that explicitly consider runtime wear progression to increase predictability and maintain lifetime reliability requirements.

For future multicores, a dedicated interconnect subsystem for on-chip monitors was found to be highly beneficial in terms of scalability, performance and area. In this thesis, such a monitor network (MNoC) is used for multicores to support selective error identification and recovery and maintain target chip reliability in the context of dynamic voltage and frequency scaling (DVFS). A selective shared memory multiprocessor recovery is performed using MNoC in which, when an error is detected, only the group of processors sharing an application with the affected processors are recovered. Although the use of DVFS in contemporary multicores provides significant protection from unpredictable thermal events, a potential side effect can be an increased processor exposure to soft errors. To address this issue, a flexible fault prevention and recovery mechanism has been developed to selectively enable a small amount of per-core dual modular redundancy (DMR) in response to increased vulnerability, as measured by the processor architectural vulnerability factor (AVF). Our new algorithm for DMR deployment aims to provide a stable effective soft error rate (SER) by using DMR in response to DVFS caused by thermal events. The algorithm is implemented in real-time on the multicore using MNoC and controller which evaluates thermal information and multicore performance statistics in addition to error information. DVFS experiments with a multicore simulator using standard benchmarks show an average 6% improvement in overall power consumption and a stable SER by using selective DMR versus continuous DMR deployment.

No description available.

Green computing, an emerging field of research that seeks to reduce excess power consumption in high performance computing (HPC), is gaining popularity among researchers. Research in this field often relies on simulation or only uses a small cluster, typically 8 or 16 nodes, because of the lack of hardware support. In contrast, System G at Virginia Tech is a 2592 processor supercomputer equipped with power aware components suitable for large scale green computing research. DIRECT is a deterministic global optimization algorithm, implemented in the mathematical software package VTDIRECT95. This thesis explores the potential energy savings for the parallel implementation of DIRECT, called pVTdirect, when used with a large scale computational biology application, parameter estimation for a budding yeast cell cycle model, on System G. Two power aware approaches for pVTdirect are developed and compared against the CPUSPEED power saving system tool. The results show that knowledge of the parallel workload of the underlying application is beneficial for power management. / Master of Science

As handheld devices have become increasingly popular, powerful programmable graphics hardware for mobile and handheld devices has been deployed. While many resources on mobile devices are limited, the predominant problem for mobile devices is their limited battery power. Several techniques have been proposed to increase the energy efficiency of mobile applications and improve battery life. In this thesis, we propose a new dynamic voltage and frequency scaling (DVFS) on Graphics Processing Units (GPU). In most cases, cues within the graphics appli- cation can be used to predict portions of a GPU that will be used or unused when the application is run. We partition the GPU into six clock domains that can be clocked at different rates. Specifically, each domain it has its own voltage and frequency set- ting based on its predicted workload to save energy without reducing applications frame rates. In addition, we propose an signature-based algorithm for predicting the workload offered to our six clock domains by a given application to decide voltage and frequency settings. We conduct experiments and compare the results of our new signature based workload prediction algorithm with some other traditional interval based workload prediction algorithms. Our results show that our signature-based prediction can save 30-50% energy without afecting application frame rates.

Les Systèmes sur Puce (SoC) ont vu leurs capacités en constante augmentation ce qui leur permet ainsi qu'aux applications s'exécutant dessus de devenir de plus en plus complexes grâce au pouvoir d'intégration de la technologie. Beaucoup de ces appareils fonctionnent sur batterie, mais puisque la technologie des batteries ne suit pas la même progression que l'intégration, à la fois le logiciel et le matériel de ces appareils doivent être économes en énergie. Nous proposons dans cette thèse un algorithme logiciel qui cherche à réduire la consommation énergétique en modifiant la fréquence et la tension des processeurs lorsque l'utilisation du système le permet. Cet algorithme n'a besoin d'aucune information sur les applications. Afin de tester et de déterminer l'efficacité de l'algorithme d'économie d'énergie proposé, nous avons besoin de plateformes de simulation rapides et précises qui supportent le changement de fréquence pour chaque processeur ou sous-système. Le bon niveau d'abstraction pour estimer la consommation d'énergie par la simulation n'est pas évident. Nous avons premièrement défini une stratégie de haut niveau de simulation qui combine la précision des simulateurs orientés matériel à la vitesse des simulateurs orientés comportement. Lorsque des estimations plus précises sont nécessaires, une simulation cycle accurate/bit accurate doit être utilisée. Toutefois, pour accélérer la simulation, des stratégies d'ordonnancement statique non compatibles avec le DVFS sont utilisées. Nous avons défini deux nouvelles approches supportant le DVFS dans ce contexte.

Increasing power dissipation is one of the most serious challenges facing designers in the microprocessor industry. Power dissipation, increasing wire delays, and increasing design complexity have forced industry to embrace multi-core architectures or chip multiprocessors (CMPs). While CMPs mitigate wire delays and design complexity, they do not directly address single-threaded performance. Additionally, programs must be parallelized, either manually or automatically, to fully exploit the performance of CMPs. Researchers have recently proposed an architecture called Explicit Data Graph Execution (EDGE) as an alternative to conventional CMPs. EDGE architectures are designed to be technology-scalable and to provide good single-threaded performance as well as exploit other types of parallelism including data-level and thread-level parallelism. In this dissertation, we examine the energy efficiency of a specific EDGE architecture called TRIPS Instruction Set Architecture (ISA) and two microarchitectures called TRIPS and TFlex that implement the TRIPS ISA. TRIPS microarchitecture is a first-generation design that proves the feasibility of the TRIPS ISA and distributed tiled microarchitectures. The second-generation TFlex microarchitecture addresses key inefficiencies of the TRIPS microarchitecture by matching the resource needs of applications to a composable hardware substrate. First, we perform a thorough power analysis of the TRIPS microarchitecture. We describe how we develop architectural power models for TRIPS. We then improve power-modeling accuracy using hardware power measurements on the TRIPS prototype combined with detailed Register Transfer Level (RTL) power models from the TRIPS design. Using these refined architectural power models and normalized power modeling methodologies, we perform a detailed performance and power comparison of the TRIPS microarchitecture with two different processors: 1) a low-end processor designed for power efficiency (ARM/XScale) and 2) a high-end superscalar processor designed for high performance (a variant of Power4). This detailed power analysis provides key insights into the advantages and disadvantages of the TRIPS ISA and microarchitecture compared to processors on either end of the performance-power spectrum. Our results indicate that the TRIPS microarchitecture achieves 11.7 times better energy efficiency compared to ARM, and approximately 12% better energy efficiency than Power4, in terms of the Energy-Delay-Squared (ED²) metric. Second, we evaluate the energy efficiency of the TFlex microarchitecture in comparison to TRIPS, ARM, and Power4. TFlex belongs to a class of microarchitectures called Composable Lightweight Processors (CLPs). CLPs are distributed microarchitectures designed with simple cores and are highly configurable at runtime to adapt to resource needs of applications. We develop power models for the TFlex microarchitecture based on the validated TRIPS power models. Our quantitative results indicate that by better matching execution resources to the needs of applications, the composable TFlex system can operate in both regimes of low power (similar to ARM) and high performance (similar to Power4). We also show that the composability feature of TFlex achieves a signification improvement (2 times) in the ED² metric compared to TRIPS. Third, using TFlex as our experimental platform, we examine the efficacy of processor composability as a potential performance-power trade-off mechanism. Most modern processors support a form of dynamic voltage and frequency scaling (DVFS) as a performance-power trade-off mechanism. Since the rate of voltage scaling has slowed significantly in recent process technologies, processor designers are in dire need of alternatives to DVFS. In this dissertation, we explore processor composability as an architectural alternative to DVFS. Through experimental results we show that processor composability achieves almost as good performance-power trade-offs as pure frequency scaling (no changes in supply voltages), and a much better performance-power trade-off compared to voltage and frequency scaling (both supply voltage and frequency change). Next, we explore the effects of additional performance-improving techniques for the TFlex system on its energy efficiency. Researchers have proposed a variety of techniques for improving the performance of the TFlex system. These include: (1) block mapping techniques to trade off intra-block concurrency with communication across the operand network; (2) predicate prediction and (3) operand multi-cast/broadcast mechanism. We examine each of these mechanisms in terms of its effect on the energy efficiency of TFlex, and our experimental results demonstrate the effects of operand communication, and speculation on the energy efficiency of TFlex. Finally, this dissertation evaluates a set of fine-grained power management (FGPM) policies for TFlex: instruction criticality and controlled speculation. These policies rely on a temporally and spatially fine-grained dynamic voltage and frequency scaling (DVFS) mechanism for improving power efficiency. The instruction criticality policy seeks to improve power efficiency by mapping critical computation in a program to higher performance-power levels, and by mapping non-critical computation to lower performance-power levels. Controlled speculation policy, on the other hand, maps blocks that are highly likely to be on correct execution path in a program to higher performance levels, and the other blocks to lower performance levels. Our experimental results indicate that idealized instruction criticality and controlled speculation policies improve the operating range and flexibility of the TFlex system. However, when the actual overheads of fine-grained DVFS, especially energy conversion losses of voltage regulator modules (VRMs), are considered the power efficiency advantages of these idealized policies quickly diminish. Our results also indicate that the current conversion efficiencies of on-chip VRMs need to improve to as high as 95% for the realistic policies to be feasible. / text