A Survey of Deep Neural Networks: Deployment Location and Underlying Hardware

Abstract

This survey paper overviews the landscape of emerging deep neural networks (neural networks for deep analytics) and explores what type of underlying hardware is likely to be used at various deployment locations: in dew, fog, and cloud computing (dew computing is performed by edge devices). The paper discusses how different architecture approaches could be used on different deployment locations, for implementing deep neural networks. These include multicore processors, manycore processors, field programmable gate arrays, and application specific integrated circuits. The classification proposed in this paper divides the existing solutions into twelve different categories. Our two-dimensional classification enables comparing the existing architectures, which are predominantly cloud based, and anticipated future architectures, which are expected to be hybrid cloud-fog-dew architectures for internet of things applications. This classification enables its users to make trade-offs between data processing bandwidth, data processing latency, and power consumption.