4,622 research outputs found

    FPGA-Based CNN Inference Accelerator Synthesized from Multi-Threaded C Software

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    A deep-learning inference accelerator is synthesized from a C-language software program parallelized with Pthreads. The software implementation uses the well-known producer/consumer model with parallel threads interconnected by FIFO queues. The LegUp high-level synthesis (HLS) tool synthesizes threads into parallel FPGA hardware, translating software parallelism into spatial parallelism. A complete system is generated where convolution, pooling and padding are realized in the synthesized accelerator, with remaining tasks executing on an embedded ARM processor. The accelerator incorporates reduced precision, and a novel approach for zero-weight-skipping in convolution. On a mid-sized Intel Arria 10 SoC FPGA, peak performance on VGG-16 is 138 effective GOPS

    Extending Reach with Technology: Seattle Opera's Multipronged Experiment to Deepen Relationships and Reach New Audiences

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    This case study describes the Seattle Opera's four-year-long effort to test which kinds of technology channels work well in audience engagement. Its experiments with technology included a simulcast of Madama Butterfly at an 8,300-capacity sports arena, interactive kiosks in the opera house lobby and online videos that took viewers behind the scenes of the opera's signature production of Wagner's Ring cycle. Every season employed at least some winning engagement tools, driven in large part by the company's efforts to gather information before determining what applications to use. Although the majority of the tools were most effective at enhancing the experience of patrons who already had a deep connection with the company, the simulcast, in project's fourth year, also brought in opera newcomers. One important lesson from the work was that effective strategies required the involvement not just of the marketing department, but of the entire organization, including its union representatives

    Chapter 3 - University Planning Standards

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    Transferring big data across the globe

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    Transmitting data via the Internet is a routine and common task for users today. The amount of data being transmitted by the average user has dramatically increased over the past few years. Transferring a gigabyte of data in an entire day was normal, however users are now transmitting multiple gigabytes in a single hour. With the influx of big data and massive scientific data sets that are measured in tens of petabytes, a user has the propensity to transfer even larger amounts of data. When transferring data sets of this magnitude on public or shared networks, the performance of all workloads in the system will be impacted. This dissertation addresses the issues and challenges inherent with transferring big data over shared networks. A survey of current transfer techniques is provided and these techniques are evaluated in simulated, experimental and live environments. The main contribution of this dissertation is the development of a new, nice model for big data transfers, which is based on a store-and-forward methodology instead of an end-to-end approach. This nice model ensures that big data transfers only occur when there is idle bandwidth that can be repurposed for these large transfers. The nice model improves overall performance and significantly reduces the transmission time for big data transfers. The model allows for efficient transfers regardless of time zone differences or variations in bandwidth between sender and receiver. Nice is the first model that addresses the challenges of transferring big data across the globe

    Testing Big Data Applications

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    Today big data has become the basis of discussion for the organizations. The big task associated with big data stream is coping with its various challenges and performing the appropriate testing for the optimal analysis of the data which may benefit the processing of various activities, especially from a business perspective. Big data term follows the massive volume of data, (might be in units of petabytes or exabytes) exceeding the processing and analytical capacity of the conventional systems and thereby raising the need for analyzing and testing the big data before applications can be put into use. Testing such huge data coming from the various number of sources like the internet, smartphones, audios, videos, media, etc. is a challenge itself. The most favourable solution to test big data follows the automated/programmed approach. This paper outlines the big data characteristics, and various challenges associated with it followed by the approach, strategy, and proposed framework for testing big data applications

    Virtual reality in theatre education and design practice - new developments and applications

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    The global use of Information and Communication Technologies (ICTs) has already established new approaches to theatre education and research, shifting traditional methods of knowledge delivery towards a more visually enhanced experience, which is especially important for teaching scenography. In this paper, I examine the role of multimedia within the field of theatre studies, with particular focus on the theory and practice of theatre design and education. I discuss various IT applications that have transformed the way we experience, learn and co-create our cultural heritage. I explore a suite of rapidly developing communication and computer-visualization techniques that enable reciprocal exchange between students, theatre performances and artefacts. Eventually, I analyse novel technology-mediated teaching techniques that attempt to provide a new media platform for visually enhanced information transfer. My findings indicate that the recent developments in the personalization of knowledge delivery, and also in student-centred study and e-learning, necessitate the transformation of the learners from passive consumers of digital products to active and creative participants in the learning experience
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