Embracing Low-Power Systems with Improvement in Security and Energy-Efficiency

As the economies around the world are aligning more towards usage of computing systems, the global energy demand for computing is increasing rapidly. Additionally, the boom in AI based applications and services has already invited the pervasion of specialized computing hardware architectures for AI (accelerators). A big chunk of research in the industry and academia is being focused on providing energy efficiency to all kinds of power hungry computing architectures. This dissertation adds to these efforts. Aggressive voltage underscaling of chips is one the effective low power paradigms of providing energy efficiency. This dissertation identifies and deals with the reliability and performance problems associated with this paradigm and innovates novel energy efficient approaches. Specifically, the properties of a low power security primitive have been improved and, higher performance has been unlocked in an AI accelerator (Google TPU) in an aggressively voltage underscaled environment. And, novel power saving opportunities have been unlocked by characterizing the usage pattern of a baseline TPU with rigorous mathematical analysis

Zuverlässige und Energieeffiziente gemischt-kritische Echtzeit On-Chip Systeme

Multi- and many-core embedded systems are increasingly becoming the target for many applications that require high performance under varying conditions. A resulting challenge is the control, and reliable operation of such complex multiprocessing architectures under changes, e.g., high temperature and degradation. In mixed-criticality systems where many applications with varying criticalities are consolidated on the same execution platform, fundamental isolation requirements to guarantee non-interference of critical functions are crucially important. While Networks-on-Chip (NoCs) are the prevalent solution to provide scalable and efficient interconnects for the multiprocessing architectures, their associated energy consumption has immensely increased. Specifically, hard real-time NoCs must manifest limited energy consumption as thermal runaway in such a core shared resource jeopardizes the whole system guarantees. Thus, dynamic energy management of NoCs, as opposed to the related work static solutions, is highly necessary to save energy and decrease temperature, while preserving essential temporal requirements. In this thesis, we introduce a centralized management to provide energy-aware NoCs for hard real-time systems. The design relies on an energy control network, developed on top of an existing switch arbitration network to allow isolation between energy optimization and data transmission. The energy control layer includes local units called Power-Aware NoC controllers that dynamically optimize NoC energy depending on the global state and applications’ temporal requirements. Furthermore, to adapt to abnormal situations that might occur in the system due to degradation, we extend the concept of NoC energy control to include the entire system scope. That is, online resource management employing hierarchical control layers to treat system degradation (imminent core failures) is supported. The mechanism applies system reconfiguration that involves workload migration. For mixed-criticality systems, it allows flexible boundaries between safety-critical and non-critical subsystems to safely apply the reconfiguration, preserving fundamental safety requirements and temporal predictability. Simulation and formal analysis-based experiments on various realistic usecases and benchmarks are conducted showing significant improvements in NoC energy-savings and in treatment of system degradation for mixed-criticality systems improving dependability over the status quo.Eingebettete Many- und Multi-core-Systeme werden zunehmend das Ziel für Anwendungen, die hohe Anfordungen unter unterschiedlichen Bedinungen haben. Für solche hochkomplexed Multi-Prozessor-Systeme ist es eine grosse Herausforderung zuverlässigen Betrieb sicherzustellen, insbesondere wenn sich die Umgebungseinflüsse verändern. In Systeme mit gemischter Kritikalität, in denen viele Anwendungen mit unterschiedlicher Kritikalität auf derselben Ausführungsplattform bedient werden müssen, sind grundlegende Isolationsanforderungen zur Gewährleistung der Nichteinmischung kritischer Funktionen von entscheidender Bedeutung. Während On-Chip Netzwerke (NoCs) häufig als skalierbare Verbindung für die Multiprozessor-Architekturen eingesetzt werden, ist der damit verbundene Energieverbrauch immens gestiegen. Daher sind dynamische Plattformverwaltungen, im Gegensatz zu den statischen, zwingend notwendig, um ein System an die oben genannten Veränderungen anzupassen und gleichzeitig Timing zu gewährleisten. In dieser Arbeit entwickeln wir energieeffiziente NoCs für harte Echtzeitsysteme. Das Design basiert auf einem Energiekontrollnetzwerk, das auf einem bestehenden Switch-Arbitration-Netzwerk entwickelt wurde, um eine Isolierung zwischen Energieoptimierung und Datenübertragung zu ermöglichen. Die Energiesteuerungsschicht umfasst lokale Einheiten, die als Power-Aware NoC-Controllers bezeichnet werden und die die NoC-Energie in Abhängigkeit vom globalen Zustand und den zeitlichen Anforderungen der Anwendungen optimieren. Darüber hinaus wird das Konzept der NoC-Energiekontrolle zur Anpassung an Anomalien, die aufgrund von Abnutzung auftreten können, auf den gesamten Systemumfang ausgedehnt. Online- Ressourcenverwaltungen, die hierarchische Kontrollschichten zur Behandlung Abnutzung (drohender Kernausfälle) einsetzen, werden bereitgestellt. Bei Systemen mit gemischter Kritikalität erlaubt es flexible Grenzen zwischen sicherheitskritischen und unkritischen Subsystemen, um die Rekonfiguration sicher anzuwenden, wobei grundlegende Sicherheitsanforderungen erhalten bleiben und Timing Vorhersehbarkeit. Experimente werden auf der Basis von Simulationen und formalen Analysen zu verschiedenen realistischen Anwendungsfallen und Benchmarks durchgeführt, die signifikanten Verbesserungen bei On-Chip Netzwerke-Energieeinsparungen und bei der Behandlung von Abnutzung für Systeme mit gemischter Kritikalität zur Verbesserung die Systemstabilität gegenüber dem bisherigen Status quo zeigen

Yield-driven power-delay-optimal CMOS full-adder design complying with automotive product specifications of PVT variations and NBTI degradations

We present the detailed results of the application of mathematical optimization algorithms to transistor sizing in a full-adder cell design, to obtain the maximum expected fabrication yield. The approach takes into account all the fabrication process parameter variations specified in an industrial PDK, in addition to operating condition range and NBTI aging. The final design solutions present transistor sizing, which depart from intuitive transistor sizing criteria and show dramatic yield improvements, which have been verified by Monte Carlo SPICE analysis

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

Architecting Efficient Data Centers.

Data center power consumption has become a key constraint in continuing to scale Internet services. As our society’s reliance on “the Cloud” continues to grow, companies require an ever-increasing amount of computational capacity to support their customers. Massive warehouse-scale data centers have emerged, requiring 30MW or more of total power capacity. Over the lifetime of a typical high-scale data center, power-related costs make up 50% of the total cost of ownership (TCO). Furthermore, the aggregate effect of data center power consumption across the country cannot be ignored. In total, data center energy usage has reached approximately 2% of aggregate consumption in the United States and continues to grow. This thesis addresses the need to increase computational efficiency to address this grow- ing problem. It proposes a new classes of power management techniques: coordinated full-system idle low-power modes to increase the energy proportionality of modern servers. First, we introduce the PowerNap server architecture, a coordinated full-system idle low- power mode which transitions in and out of an ultra-low power nap state to save power during brief idle periods. While effective for uniprocessor systems, PowerNap relies on full-system idleness and we show that such idleness disappears as the number of cores per processor continues to increase. We expose this problem in a case study of Google Web search in which we demonstrate that coordinated full-system active power modes are necessary to reach energy proportionality and that PowerNap is ineffective because of a lack of idleness. To recover full-system idleness, we introduce DreamWeaver, architectural support for deep sleep. DreamWeaver allows a server to exchange latency for full-system idleness, allowing PowerNap-enabled servers to be effective and provides a better latency- power savings tradeoff than existing approaches. Finally, this thesis investigates workloads which achieve efficiency through methodical cluster provisioning techniques. Using the popular memcached workload, this thesis provides examples of provisioning clusters for cost-efficiency given latency, throughput, and data set size targets.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91499/1/meisner_1.pd

Computational Sprinting: Exceeding Sustainable Power in Thermally Constrained Systems

Although process technology trends predict that transistor sizes will continue to shrink for a few more generations, voltage scaling has stalled and thus future chips are projected to be increasingly more power hungry than previous generations. Particularly in mobile devices which are severely cooling constrained, it is estimated that the peak operation of a future chip could generate heat ten times faster than than the device can sustainably vent. However, many mobile applications do not demand sustained performance; rather they comprise short bursts of computation in response to sporadic user activity. To improve responsiveness for such applications, this dissertation proposes computational sprinting, in which a system greatly exceeds sustainable power margins (by up to 10Ã?) to provide up to a few seconds of high-performance computation when a user interacts with the device. Computational sprinting exploits the material property of thermal capacitance to temporarily store the excess heat generated when sprinting. After sprinting, the chip returns to sustainable power levels and dissipates the stored heat when the system is idle. This dissertation: (i) broadly analyzes thermal, electrical, hardware, and software considerations to analyze the feasibility of engineering a system which can provide the responsiveness of a plat- form with 10Ã? higher sustainable power within today\u27s cooling constraints, (ii) leverages existing sources of thermal capacitance to demonstrate sprinting on a real system today, and (iii) identifies the energy-performance characteristics of sprinting operation to determine runtime sprint pacing policies

Energy Efficient Scheduling for Real-Time Systems

The goal of this dissertation is to extend the state of the art in real-time scheduling algorithms to achieve energy efficiency. Currently, Pfair scheduling is one of the few scheduling frameworks which can optimally schedule a periodic real-time taskset on a multiprocessor platform. Despite the theoretical optimality, there exist large concerns about efficiency and applicability of Pfair scheduling in practical situations. This dissertation studies and proposes solutions to such efficiency and applicability concerns. This dissertation also explores temperature aware energy management in the domain of real-time scheduling. The thesis of this dissertation is: the implementation efficiency of Pfair scheduling algorithms can be improved. Further, temperature awareness of a real-time system can be improved while considering variation of task execution times to reduce energy consumption. This thesis is established through research in a number of directions. First, we explore the applicability of Dynamic Voltage and Frequency Scaling (DVFS) feature in the underlying platform, within Pfair scheduled systems. We propose techniques to reduce energy consumption in Pfair scheduling by using DVFS. Next, we explore the problem of quantum size selection in Pfair scheduled system so that runtime overheads are minimized. We also propose a hardware design for a central Pfair scheduler core in a multiprocessor system to minimized the overheads and energy consumption of Pfair scheduling. Finally, we propose a temperature aware energy management scheme for tasks with varying execution times