1,474 research outputs found

    Embracing Visual Experience and Data Knowledge: Efficient Embedded Memory Design for Big Videos and Deep Learning

    Get PDF
    Energy efficient memory designs are becoming increasingly important, especially for applications related to mobile video technology and machine learning. The growing popularity of smart phones, tablets and other mobile devices has created an exponential demand for video applications in today?s society. When mobile devices display video, the embedded video memory within the device consumes a large amount of the total system power. This issue has created the need to introduce power-quality tradeoff techniques for enabling good quality video output, while simultaneously enabling power consumption reduction. Similarly, power efficiency issues have arisen within the area of machine learning, especially with applications requiring large and fast computation, such as neural networks. Using the accumulated data knowledge from various machine learning applications, there is now the potential to create more intelligent memory with the capability for optimized trade-off between energy efficiency, area overhead, and classification accuracy on the learning systems. In this dissertation, a review of recently completed works involving video and machine learning memories will be covered. Based on the collected results from a variety of different methods, including: subjective trials, discovered data-mining patterns, software simulations, and hardware power and performance tests, the presented memories provide novel ways to significantly enhance power efficiency for future memory devices. An overview of related works, especially the relevant state-of-the-art research, will be referenced for comparison in order to produce memory design methodologies that exhibit optimal quality, low implementation overhead, and maximum power efficiency.National Science FoundationND EPSCoRCenter for Computationally Assisted Science and Technology (CCAST

    A survey on cost-effective context-aware distribution of social data streams over energy-efficient data centres

    Get PDF
    Social media have emerged in the last decade as a viable and ubiquitous means of communication. The ease of user content generation within these platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges, including derivation of real-time meaningful insights from effectively gathered social information, as well as a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general. In this article we present a comprehensive survey that outlines the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centres supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. We systematize the existing literature and proceed to identify and analyse the main research points and industrial efforts in the area as far as modelling, simulation and performance evaluation are concerned

    System-Level Energy-Aware Design of Cyber-Physical Systems

    Get PDF
    In this technical report we present the work conducted during the first part of the PhD thesis “System-Level Energy-Aware Design of Cyber-Physical Systems”. We present the application of modelling techniques and methodologies to study energy consumption during the design and implementation of cyber-physical systems. This study is made from the electro-mechanical and computation angle. Additionally we present a setup that allows the combination of abstract models with hardware and software preliminary realizations. This allows a stepwise model to implementation transformation and improved model accuracy. Some of these techniques have been applied to the case study e-Stocking and others have been studied with more simple experimental setups.In addition to the scientific content, we also present a description of the envisioned future work and the plans that will lead to completion of this PhD thesis by April 2015
    • …
    corecore