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Digital Neural Networks for New Media

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Chips 2020

Part of the book series: The Frontiers Collection ((FRONTCOLL))

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Abstract

Neural Networks perform computationally intensive tasks offering smart solutions for many new media applications. A number of analog and mixed digital/analog implementations have been proposed to smooth the algorithmic gap. But gradually, the digital implementation has become feasible, and the dedicated neural processor is on the horizon. A notable example is the Cellular Neural Network (CNN). The analog direction has matured for low-power, smart vision sensors; the digital direction is gradually being shaped into an IP-core for algorithm acceleration, especially for use in FPGA-based high-performance systems. The chapter discusses the next step towards a flexible and scalable multi-core engine using Application-Specific Integrated Processors (ASIP). This topographic engine can serve many new media tasks, as illustrated by novel applications in Homeland Security. We conclude with a view on the CNN kaleidoscope for the year 2020.

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Authors and Affiliations

  1. Department of Electrical & Information Technology, Lund University, 118, 22100, Lund, Sweden

    Lambert Spaanenburg & Suleyman Malki

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  1. Lambert Spaanenburg
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  2. Suleyman Malki
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Correspondence to Lambert Spaanenburg .

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  1. Institute for Microelectronics, Leonberger Str. 5, Sindelfingen, 71063, Germany

    Bernd Hoefflinger

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Spaanenburg, L., Malki, S. (2011). Digital Neural Networks for New Media. In: Hoefflinger, B. (eds) Chips 2020. The Frontiers Collection. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23096-7_16

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  • DOI: https://doi.org/10.1007/978-3-642-23096-7_16

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