Paper Info
Title
Moving convolutional neural networks to embedded systems: the alexnet and VGG-16 case.
Abstract
Execution of deep learning solutions is mostly restricted to high performing computing platforms, e.g., those endowed with GPUs or FPGAs, due to the high demand on computation and memory such solutions require. Despite the fact that dedicated hardware is nowadays subject of research and effective solutions exist, we envision a future where deep learning solutions -here Convolutional Neural Networks (CNNs)- are mostly executed by low-cost off-the shelf embedded platforms already available in the market. This paper moves in this direction and aims at filling the gap between CNNs and embedded systems by introducing a methodology for the design and porting of CNNs to limited in resources embedded systems. In order to achieve this goal we employ approximate computing techniques to reduce the computational load and memory occupation of the deep learning architecture by compromising accuracy with memory and computation. The proposed methodology has been validated on two well-know CNNs, i.e., AlexNet and VGG-16, applied to an image-recognition application and ported to two relevant off-the-shelf embedded platforms.
Year
DOI
Venue
2018
10.1109/IPSN.2018.00049
IPSN
Keywords
Field
DocType
Embedded Systems, Deep Learning, Convolutional Neural Networks, Approximate Computing
Convolutional code,Convolutional neural network,Computer science,Field-programmable gate array,Feature extraction,Memory management,Porting,Artificial intelligence,Deep learning,Computation,Embedded system
Conference
ISBN
Citations 
PageRank 
978-1-5386-5298-5
3
0.38
References 
Authors
26
3
Name
Order
Citations
PageRank
Cesare Alippi11040115.84
Simone Disabato241.40
Manuel Roveri327230.19