60 research outputs found
Energy-precision tradeoffs in the graphics pipeline
The energy consumption of a graphics processing unit (GPU) is an important factor in its design, whether for a server, desktop, or mobile device. Mobile products, such as smart phones, tablets, and laptop computers, rely on batteries to function; the less the demand for power is on these batteries, the longer they will last before needing to be recharged. GPUs used in servers and desktops, while not dependent on a battery for operation, are still limited by the efficiency of power supplies and heat dissipation techniques. In this dissertation, I propose to lower the energy consumption of GPUs by reducing the precision of floating-point arithmetic in the graphics pipeline and the data sent and stored on- and off-chip. The key idea behind this work is twofold: energy can be saved through a systematic and targeted reduction in the number of bits 1) computed and 2) communicated. Reducing the number of bits computed will necessarily reduce either the precision or range of a floating point number. I focus on saving energy by way of reducing precision, which can exploit the over-provisioning of bits in many stages of the graphics pipeline. Reducing the number of bits communicated takes several forms. First, I propose enhancements to existing compression schemes for off-chip buffers to save bandwidth. I also suggest a simple extension that exploits unused bits in reduced-precision data undergoing compression. Finally, I present techniques for saving energy in on-chip communication of reduced-precision data. By designing and simulating variable-precision arithmetic circuits with promising energy versus precision characteristics and tradeoffs, I have developed an energy model for GPUs. Using this model and my techniques, I have shown that significant savings (up to 70% in computation in the vertex and pixel shader stages) are possible by reducing the precision of the arithmetic. Further, my compression approaches have enabled improvements of 1.26x over past work, and a general-purpose compressor design has achieved bandwidth savings of 34%, 87%, and 65% for color, depth, and geometry data, respectively, which is competitive with past work. Lastly, an initial exploration in signal gating unused lines in on-chip buses has suggested savings of 13-48% for the tested applications' traffic from a multiprocessor's register file to its L1 cache
Applications in Electronics Pervading Industry, Environment and Society
This book features the manuscripts accepted for the Special Issue “Applications in Electronics Pervading Industry, Environment and Society—Sensing Systems and Pervasive Intelligence” of the MDPI journal Sensors. Most of the papers come from a selection of the best papers of the 2019 edition of the “Applications in Electronics Pervading Industry, Environment and Society” (APPLEPIES) Conference, which was held in November 2019. All these papers have been significantly enhanced with novel experimental results. The papers give an overview of the trends in research and development activities concerning the pervasive application of electronics in industry, the environment, and society. The focus of these papers is on cyber physical systems (CPS), with research proposals for new sensor acquisition and ADC (analog to digital converter) methods, high-speed communication systems, cybersecurity, big data management, and data processing including emerging machine learning techniques. Physical implementation aspects are discussed as well as the trade-off found between functional performance and hardware/system costs
NON-VERBAL COMMUNICATION WITH PHYSIOLOGICAL SENSORS. THE AESTHETIC DOMAIN OF WEARABLES AND NEURAL NETWORKS
Historically, communication implies the transfer of information between bodies, yet this
phenomenon is constantly adapting to new technological and cultural standards. In a
digital context, it’s commonplace to envision systems that revolve around verbal modalities.
However, behavioural analysis grounded in psychology research calls attention to
the emotional information disclosed by non-verbal social cues, in particular, actions that
are involuntary. This notion has circulated heavily into various interdisciplinary computing
research fields, from which multiple studies have arisen, correlating non-verbal
activity to socio-affective inferences. These are often derived from some form of motion
capture and other wearable sensors, measuring the ‘invisible’ bioelectrical changes that
occur from inside the body.
This thesis proposes a motivation and methodology for using physiological sensory
data as an expressive resource for technology-mediated interactions. Initialised from a
thorough discussion on state-of-the-art technologies and established design principles
regarding this topic, then applied to a novel approach alongside a selection of practice
works to compliment this. We advocate for aesthetic experience, experimenting with
abstract representations. Atypically from prevailing Affective Computing systems, the
intention is not to infer or classify emotion but rather to create new opportunities for rich
gestural exchange, unconfined to the verbal domain.
Given the preliminary proposition of non-representation, we justify a correspondence
with modern Machine Learning and multimedia interaction strategies, applying an iterative,
human-centred approach to improve personalisation without the compromising
emotional potential of bodily gesture. Where related studies in the past have successfully
provoked strong design concepts through innovative fabrications, these are typically limited
to simple linear, one-to-one mappings and often neglect multi-user environments;
we foresee a vast potential. In our use cases, we adopt neural network architectures to
generate highly granular biofeedback from low-dimensional input data.
We present the following proof-of-concepts: Breathing Correspondence, a wearable
biofeedback system inspired by Somaesthetic design principles; Latent Steps, a real-time auto-encoder to represent bodily experiences from sensor data, designed for dance performance;
and Anti-Social Distancing Ensemble, an installation for public space interventions,
analysing physical distance to generate a collective soundscape. Key findings are
extracted from the individual reports to formulate an extensive technical and theoretical
framework around this topic. The projects first aim to embrace some alternative perspectives
already established within Affective Computing research. From here, these concepts
evolve deeper, bridging theories from contemporary creative and technical practices with
the advancement of biomedical technologies.Historicamente, os processos de comunicação implicam a transferência de informação
entre organismos, mas este fenómeno está constantemente a adaptar-se a novos padrões
tecnológicos e culturais. Num contexto digital, é comum encontrar sistemas que giram
em torno de modalidades verbais. Contudo, a análise comportamental fundamentada
na investigação psicológica chama a atenção para a informação emocional revelada por
sinais sociais não verbais, em particular, acções que são involuntárias. Esta noção circulou
fortemente em vários campos interdisciplinares de investigação na área das ciências da
computação, dos quais surgiram múltiplos estudos, correlacionando a actividade nãoverbal
com inferências sócio-afectivas. Estes são frequentemente derivados de alguma
forma de captura de movimento e sensores “wearable”, medindo as alterações bioeléctricas
“invisíveis” que ocorrem no interior do corpo.
Nesta tese, propomos uma motivação e metodologia para a utilização de dados sensoriais
fisiológicos como um recurso expressivo para interacções mediadas pela tecnologia.
Iniciada a partir de uma discussão aprofundada sobre tecnologias de ponta e princípios
de concepção estabelecidos relativamente a este tópico, depois aplicada a uma nova abordagem,
juntamente com uma selecção de trabalhos práticos, para complementar esta.
Defendemos a experiência estética, experimentando com representações abstractas. Contrariamente
aos sistemas de Computação Afectiva predominantes, a intenção não é inferir
ou classificar a emoção, mas sim criar novas oportunidades para uma rica troca gestual,
não confinada ao domínio verbal.
Dada a proposta preliminar de não representação, justificamos uma correspondência
com estratégias modernas de Machine Learning e interacção multimédia, aplicando uma
abordagem iterativa e centrada no ser humano para melhorar a personalização sem o
potencial emocional comprometedor do gesto corporal. Nos casos em que estudos anteriores
demonstraram com sucesso conceitos de design fortes através de fabricações
inovadoras, estes limitam-se tipicamente a simples mapeamentos lineares, um-para-um,
e muitas vezes negligenciam ambientes multi-utilizadores; com este trabalho, prevemos
um potencial alargado. Nos nossos casos de utilização, adoptamos arquitecturas de redes
neurais para gerar biofeedback altamente granular a partir de dados de entrada de baixa dimensão.
Apresentamos as seguintes provas de conceitos: Breathing Correspondence, um sistema
de biofeedback wearable inspirado nos princípios de design somaestético; Latent
Steps, um modelo autoencoder em tempo real para representar experiências corporais
a partir de dados de sensores, concebido para desempenho de dança; e Anti-Social Distancing
Ensemble, uma instalação para intervenções no espaço público, analisando a
distância física para gerar uma paisagem sonora colectiva. Os principais resultados são
extraídos dos relatórios individuais, para formular um quadro técnico e teórico alargado
para expandir sobre este tópico. Os projectos têm como primeiro objectivo abraçar algumas
perspectivas alternativas às que já estão estabelecidas no âmbito da investigação
da Computação Afectiva. A partir daqui, estes conceitos evoluem mais profundamente,
fazendo a ponte entre as teorias das práticas criativas e técnicas contemporâneas com o
avanço das tecnologias biomédicas
A Practical Hardware Implementation of Systemic Computation
It is widely accepted that natural computation, such as brain computation, is far superior to typical computational approaches addressing tasks such as learning and parallel processing. As conventional silicon-based technologies are about to reach their physical limits, researchers have drawn inspiration from nature to found new computational paradigms. Such a newly-conceived paradigm is Systemic Computation (SC). SC is a bio-inspired model of computation. It incorporates natural characteristics and defines a massively parallel non-von Neumann computer architecture that can model natural systems efficiently. This thesis investigates the viability and utility of a Systemic Computation hardware implementation, since prior software-based approaches have proved inadequate in terms of performance and flexibility. This is achieved by addressing three main research challenges regarding the level of support for the natural properties of SC, the design of its implied architecture and methods to make the implementation practical and efficient. Various hardware-based approaches to Natural Computation are reviewed and their compatibility and suitability, with respect to the SC paradigm, is investigated. FPGAs are identified as the most appropriate implementation platform through critical evaluation and the first prototype Hardware Architecture of Systemic computation (HAoS) is presented. HAoS is a novel custom digital design, which takes advantage of the inbuilt parallelism of an FPGA and the highly efficient matching capability of a Ternary Content Addressable Memory. It provides basic processing capabilities in order to minimize time-demanding data transfers, while the optional use of a CPU provides high-level processing support. It is optimized and extended to a practical hardware platform accompanied by a software framework to provide an efficient SC programming solution. The suggested platform is evaluated using three bio-inspired models and analysis shows that it satisfies the research challenges and provides an effective solution in terms of efficiency versus flexibility trade-off
Digitalization and Development
This book examines the diffusion of digitalization and Industry 4.0 technologies in Malaysia by focusing on the ecosystem critical for its expansion. The chapters examine the digital proliferation in major sectors of agriculture, manufacturing, e-commerce and services, as well as the intermediary organizations essential for the orderly performance of socioeconomic agents.
The book incisively reviews policy instruments critical for the effective and orderly development of the embedding organizations, and the regulatory framework needed to quicken the appropriation of socioeconomic synergies from digitalization and Industry 4.0 technologies. It highlights the importance of collaboration between government, academic and industry partners, as well as makes key recommendations on how to encourage adoption of IR4.0 technologies in the short- and long-term.
This book bridges the concepts and applications of digitalization and Industry 4.0 and will be a must-read for policy makers seeking to quicken the adoption of its technologies
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