Survey of deployment locations and underlying hardware architectures for contemporary deep neural networks

Miloš Kotlar, Dragan Bojić, Marija Punt and Veljko Milutinović

International Journal of Distributed Sensor Networks, 2019, vol. 15, issue 8, 1550147719868669

Abstract: This article overviews the emerging use of deep neural networks in data analytics and explores which type of underlying hardware and architectural approach is best used in various deployment locations when implementing deep neural networks. The locations which are discussed are in the cloud, fog, and dew computing (dew computing is performed by end devices). Covered architectural approaches include multicore processors (central processing unit), manycore processors (graphics processing unit), field programmable gate arrays, and application-specific integrated circuits. The proposed classification in this article divides the existing solutions into 12 different categories, organized in two dimensions. The proposed classification allows a comparison of existing architectures, which are predominantly cloud-based, and anticipated future architectures, which are expected to be hybrid cloud-fog-dew architectures for applications in Internet of Things and Wireless Sensor Networks. Researchers interested in studying trade-offs among data processing bandwidth, data processing latency, and processing power consumption would benefit from the classification made in this article.

Keywords: Application-specific integrated circuit; big data; cloud computing; central processing unit; deep neural networks; dew computing; edge computing; fog computing; field programmable gate array; graphics processing unit (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:sae:intdis:v:15:y:2019:i:8:p:1550147719868669

DOI: 10.1177/1550147719868669

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