Design Techniques for Energy-Quality Scalable Digital Systems

Energy efficiency is one of the key design goals in modern computing. Increasingly complex tasks are being executed in mobile devices and Internet of Things end-nodes, which are expected to operate for long time intervals, in the orders of months or years, with the limited energy budgets provided by small form-factor batteries. Fortunately, many of such tasks are error resilient, meaning that they can toler- ate some relaxation in the accuracy, precision or reliability of internal operations, without a significant impact on the overall output quality. The error resilience of an application may derive from a number of factors. The processing of analog sensor inputs measuring quantities from the physical world may not always require maximum precision, as the amount of information that can be extracted is limited by the presence of external noise. Outputs destined for human consumption may also contain small or occasional errors, thanks to the limited capabilities of our vision and hearing systems. Finally, some computational patterns commonly found in domains such as statistics, machine learning and operational research, naturally tend to reduce or eliminate errors. Energy-Quality (EQ) scalable digital systems systematically trade off the quality of computations with energy efficiency, by relaxing the precision, the accuracy, or the reliability of internal software and hardware components in exchange for energy reductions. This design paradigm is believed to offer one of the most promising solutions to the impelling need for low-energy computing. Despite these high expectations, the current state-of-the-art in EQ scalable design suffers from important shortcomings. First, the great majority of techniques proposed in literature focus only on processing hardware and software components. Nonetheless, for many real devices, processing contributes only to a small portion of the total energy consumption, which is dominated by other components (e.g. I/O, memory or data transfers). Second, in order to fulfill its promises and become diffused in commercial devices, EQ scalable design needs to achieve industrial level maturity. This involves moving from purely academic research based on high-level models and theoretical assumptions to engineered flows compatible with existing industry standards. Third, the time-varying nature of error tolerance, both among different applications and within a single task, should become more central in the proposed design methods. This involves designing “dynamic” systems in which the precision or reliability of operations (and consequently their energy consumption) can be dynamically tuned at runtime, rather than “static” solutions, in which the output quality is fixed at design-time. This thesis introduces several new EQ scalable design techniques for digital systems that take the previous observations into account. Besides processing, the proposed methods apply the principles of EQ scalable design also to interconnects and peripherals, which are often relevant contributors to the total energy in sensor nodes and mobile systems respectively. Regardless of the target component, the presented techniques pay special attention to the accurate evaluation of benefits and overheads deriving from EQ scalability, using industrial-level models, and on the integration with existing standard tools and protocols. Moreover, all the works presented in this thesis allow the dynamic reconfiguration of output quality and energy consumption. More specifically, the contribution of this thesis is divided in three parts. In a first body of work, the design of EQ scalable modules for processing hardware data paths is considered. Three design flows are presented, targeting different technologies and exploiting different ways to achieve EQ scalability, i.e. timing-induced errors and precision reduction. These works are inspired by previous approaches from the literature, namely Reduced-Precision Redundancy and Dynamic Accuracy Scaling, which are re-thought to make them compatible with standard Electronic Design Automation (EDA) tools and flows, providing solutions to overcome their main limitations. The second part of the thesis investigates the application of EQ scalable design to serial interconnects, which are the de facto standard for data exchanges between processing hardware and sensors. In this context, two novel bus encodings are proposed, called Approximate Differential Encoding and Serial-T0, that exploit the statistical characteristics of data produced by sensors to reduce the energy consumption on the bus at the cost of controlled data approximations. The two techniques achieve different results for data of different origins, but share the common features of allowing runtime reconfiguration of the allowed error and being compatible with standard serial bus protocols. Finally, the last part of the manuscript is devoted to the application of EQ scalable design principles to displays, which are often among the most energy- hungry components in mobile systems. The two proposals in this context leverage the emissive nature of Organic Light-Emitting Diode (OLED) displays to save energy by altering the displayed image, thus inducing an output quality reduction that depends on the amount of such alteration. The first technique implements an image-adaptive form of brightness scaling, whose outputs are optimized in terms of balance between power consumption and similarity with the input. The second approach achieves concurrent power reduction and image enhancement, by means of an adaptive polynomial transformation. Both solutions focus on minimizing the overheads associated with a real-time implementation of the transformations in software or hardware, so that these do not offset the savings in the display. For each of these three topics, results show that the aforementioned goal of building EQ scalable systems compatible with existing best practices and mature for being integrated in commercial devices can be effectively achieved. Moreover, they also show that very simple and similar principles can be applied to design EQ scalable versions of different system components (processing, peripherals and I/O), and to equip these components with knobs for the runtime reconfiguration of the energy versus quality tradeoff

Business model benchmarking : how a machine learning-based tool can support business model innovation

An increasing body of literature has developed around business models and business model innovation in recent years, establishing the concepts’ relevance for the competitiveness of a firm. The process of innovating a business model is less researched and still constitutes a com-plex and challenging task for businesses. Despite the advancements of technology, only few tools have been developed to support said process. Thus, present work focuses on how this process can be facilitated by introducing the concept of benchmarking and complementing it through a machine learning model. A machine learning supported multi-case theory building approach was followed to indicate how benchmarking business models might benefit compa-nies and to arrive at a machine learning model supporting this endeavour. The business models of 306 firms were evaluated to do so. Benchmarking business models can benefit a company by monitoring and learning from other organizations to spark innovation and support idea cre-ation. The machine learning model predicts business model patterns which are used to make processes across companies comparable. The present thesis results in a business model bench-marking tool which supports managers and entrepreneurs alike in their quest of business model innovation.Nos últimos anos, um número significativo de estudos foram desenvolvidos em torno de modelos de negócios e na inovação dos mesmos, afirmando a relevância destes conceitos para a competitividade de uma empresa. O processo de inovação de um modelo de negócio não só é pouco estudado, como ainda constitui uma tarefa complexa e desafiadora para as empresas. Apesar dos avanços da tecnologia, apenas algumas meios foram desenvolvidos para dar suporte ao referido processo. Assim, a presente dissertação realça como este processo pode ser facilitado, introduzindo o conceito de benchmarking e completando-o através do modelo ‘machine learning model’. Multi-case theory building foi apoiado pelo modelo ‘machine learning model’ para indicar como a análise de benchmark dos modelos de negócio podem beneficiar as empresas e para alcançar um machine learning model que suporte esse esforço. Nesse sentido, os modelos de negócio de 306 empresas foram avaliados. A aprendizagem e monitorização de outras organizações pode beneficiar o modelo de negócio de uma empresa, estimulando a inovação e a criação de novas ideias. Machine learning model é um modelo que prevê os padrões de modelos de negócio que são utilizados para tornar os processos entre empresas comparáveis. A presente dissertação desenvolveu um método de análise de benchmark de modelos de negócio que apoia gerentes e empreendedores na pesquisa pela inovação de modelos de negócio

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Rural realities between crisis and normality : livelihood strategies in Angola, 1975-2008

In this thesis I examined the ways in which rural people in Huíla province, Angola, have dealt with crises and adapted their livelihoods accordingly. These responses and adaptations to crises are then juxtaposed against the variety of interventions by state and aid agencies which affect rural livelihoods in broad terms. The Angolan population has lived through a long history of conflict, starting with an independence war against Portugal since 1961, and evolving into a civil war from the start of independence in 1975 lasting until 2002. Throughout this violent history, humanitarian actors made a significant range of interventions with the intention to alleviate the suffering of the country’s population and help them rebuild their lives and livelihoods after the end of the war in 2002. In this thesis I analysed these interventions, especially related to the recovery of rural livelihoods, to understand the assumptions underlying them, as well as their outcomes. The core question that guided the research underlying this thesis was the following: How are people’s livelihoods affected in times of crisis, and how do aid interventions influence the livelihood options that people have in Huíla province, Angola? In my analysis I used the concept of a humanitarian arena in order to 1) acknowledge the diversity of actors that shape the outcomes of aid processes, 2) move away from normative explanations of aid and rather focus on its everyday practices, and 3) focus on the negotiations, experiences and agency of the actors at the interface at which processes of aid are shaped. This builds on an actor-oriented approach which calls attention to agency, actors and interfaces to explain that planned development is rarely a linear process but rather a site of struggles and negotiations amongst a variety of actors. The fieldwork underlying this thesis was done in six villages with different experiences of conflict, aid, and livelihoods. I look at the concept of livelihoods as comprising the assets and activities that people employ to make a living, and the access to these (Ellis 2000a, 10). I deviate from the policy construct of a livelihoods approach, which tends to define livelihoods by a restricted focus on the various capitals. Rather, I have looked at livelihoods as being more flexible in nature in which the disappearance of some assets can be dealt with by strengthening others. Livelihoods are fluid and flexible, and certainly have to be so in situations of crisis and conflict. Aid in this thesis is seen as one of the many strategies that people rely on for their survival in times of crisis. Humanitarian aid is analysed in this thesis and in particular its changing practice due to the more protracted nature of the crisis situations it operates in. This has demanded the incorporation of rehabilitation and development approaches, translated in a stronger engagement with the state, and a shift from a focus on individuals to society. I question the practices of Linking Relief, Rehabilitation and Development (LRRD) approaches when it is uncertain whether intervention objectives can be attained or processes have to be abandoned. This thesis sheds light on the consequences of such unfinished LRRD processes. This research has analysed the everyday realities and outcomes of post-war recovery and reconstruction practices by aid agencies and the Angolan state. It shows how aid programmes that focus on resettlement of conflict-affected populations and rebuilding of rural livelihoods can have unintended consequences when little attention is given to follow up of these activities that were assumed to lead to development. At the same time, the research shows how state post-war reconstruction efforts by the state largely bypass rural areas, or at worst even lead to renewed displacement from land and livelihoods. Therefore, the title of this thesis reiterates that livelihoods in conflict and post-conflict situations continuously move between crisis and normality, yet that this phenomenon is not necessarily linked to war itself. Also, the use of the word normality underscores the underlying assumptions on which aid interventions are designed in processes of livelihood recovery: a return to normality. One can question what ‘normal’ livelihoods are in the Angolan context of long-term instability. Also, who defines normality? As shown in this thesis, aid actors have had quite uniform and fixed assumptions and interpretations about what ‘normal’ rural livelihoods should look like, reflected in the one-size-fits-all interventions that consequently took place. </p