Next Article in Journal
A Rigid Motion Artifact Reduction Method for CT Based on Blind Deconvolution
Previous Article in Journal
γ-Graphs of Trees
Previous Article in Special Issue
Mapping a Guided Image Filter on the HARP Reconfigurable Architecture Using OpenCL
Open AccessFeature PaperReview

A Survey of Convolutional Neural Networks on Edge with Reconfigurable Computing

INESC-ID, Instituto Superior de Engenharia de Lisboa, Instituto Politécnico de Lisboa, 1500-335 Lisboa, Portugal
Algorithms 2019, 12(8), 154; https://doi.org/10.3390/a12080154
Received: 24 June 2019 / Revised: 27 July 2019 / Accepted: 30 July 2019 / Published: 31 July 2019
(This article belongs to the Special Issue High Performance Reconfigurable Computing)
The convolutional neural network (CNN) is one of the most used deep learning models for image detection and classification, due to its high accuracy when compared to other machine learning algorithms. CNNs achieve better results at the cost of higher computing and memory requirements. Inference of convolutional neural networks is therefore usually done in centralized high-performance platforms. However, many applications based on CNNs are migrating to edge devices near the source of data due to the unreliability of a transmission channel in exchanging data with a central server, the uncertainty about channel latency not tolerated by many applications, security and data privacy, etc. While advantageous, deep learning on edge is quite challenging because edge devices are usually limited in terms of performance, cost, and energy. Reconfigurable computing is being considered for inference on edge due to its high performance and energy efficiency while keeping a high hardware flexibility that allows for the easy adaption of the target computing platform to the CNN model. In this paper, we described the features of the most common CNNs, the capabilities of reconfigurable computing for running CNNs, the state-of-the-art of reconfigurable computing implementations proposed to run CNN models, as well as the trends and challenges for future edge reconfigurable platforms. View Full-Text
Keywords: deep learning; convolutional neural network; reconfigurable computing; field-programmable gate array; edge inference deep learning; convolutional neural network; reconfigurable computing; field-programmable gate array; edge inference
Show Figures

Figure 1

MDPI and ACS Style

Véstias, M.P. A Survey of Convolutional Neural Networks on Edge with Reconfigurable Computing. Algorithms 2019, 12, 154.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map

1
Algorithms, EISSN 1999-4893, Published by MDPI AG
Back to TopTop