Paper Info
Title
An Overview Of Next-Generation Architectures For Machine Learning: Roadmap, Opportunities And Challenges In The Iot Era
Abstract
The number of connected Internet of Things (IoT) devices are expected to reach over 20 billion by 2020. These range from basic sensor nodes that log and report the data to the ones that are capable of processing the incoming information and taking an action accordingly. Machine learning, and in particular deep learning, is the de facto processing paradigm for intelligently processing these immense volumes of data. However, the resource inhibited environment of loT devices, owing to their limited energy budget and low compute capabilities, render them a challenging platform for deployment of desired data analytics. This paper provides an overview of the current and emerging trends in designing highly efficient, reliable, secure and scalable machine learning architectures for such devices. The paper highlights the focal challenges and obstacles being faced by the community in achieving its desired goals. The paper further presents a roadmap that can help in addressing the highlighted challenges and thereby designing scalable, high-performance, and energy efficient architectures for performing machine learning on the edge.
Year
Venue
Keywords
2018
PROCEEDINGS OF THE 2018 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE)
Deep Learning, Convolutional Neural Networks, IoT, Machine Learning
Field
DocType
ISSN
Software deployment,Data analysis,Convolutional neural network,Efficient energy use,Computer science,Internet of Things,Automation,Artificial intelligence,Deep learning,Machine learning,Scalability
Conference
1530-1591
Citations 
PageRank 
References 
6
0.41
0
Authors
7
Name
Order
Citations
PageRank
Muhammad Shafique11945157.67
Theocharis Theocharides220526.83
Christos-Savvas Bouganis3377.60
Muhammad Abdullah Hanif47118.12
Faiq Khalid Lodhi55410.33
Rehan Hafiz6287.93
Semeen Rehman744731.92