publication . Article . Other literature type . 2018

A Design Space Exploration Framework for Convolutional Neural Networks Implemented on Edge Devices

Anastasios Bartsokas; Dimitrios Soudris; Foivos Tsimpourlas; Lazaros Papadopoulos;
Open Access English
  • Published: 01 Nov 2018 Journal: IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, volume 37, issue 11, pages 2,212-2,221 (issn: 0278-0070, eissn: 1937-4151, Copyright policy)
Deploying convolutional neural networks (CNNs) in embedded devices that operate at the edges of Internet of Things (IoT) networks provides various advantages in terms of performance, energy efficiency, and security in comparison with the alternative approach of transmitting large volumes of data for processing to the cloud. However, the implementation of CNNs on low power embedded devices is challenging due to the limited computational resources they provide and to the large resource requirements of state-of-the-art CNNs. In this paper, we propose a framework for the efficient deployment of CNNs in low power processor-based architectures used as edge devices in ...
Persistent Identifiers
free text keywords: Distributed computing, Cloud computing, business.industry, business, Energy consumption, Computer science, Edge device, Efficient energy use, Design space exploration, Convolutional neural network, Implementation, Resource management
Funded by
Software Development toolKit for Energy optimization and technical Debt elimination
  • Funder: European Commission (EC)
  • Project Code: 780572
  • Funding stream: H2020 | RIA
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