Squeeze-and-Excitation SqueezeNext: An Efficient DNN for Hardware Deployment

If you need an accessible version of this item, please email your request to digschol@iu.edu so that they may create one and provide it to you.
Date
2020-01
Language
English
Embargo Lift Date
Committee Members
Degree
Degree Year
Department
Grantor
Journal Title
Journal ISSN
Volume Title
Found At
IEEE
Abstract

Convolution neural network is being used in field of autonomous driving vehicles or driver assistance systems (ADAS), and has achieved great success. Before the convolution neural network, traditional machine learning algorithms helped the driver assistance systems. Currently, there is a great exploration being done in architectures like MobileNet, SqueezeNext & SqueezeNet. It improved the CNN architectures and made it more suitable to implement on real-time embedded systems. This paper proposes an efficient and a compact CNN to ameliorate the performance of existing CNN architectures. The intuition behind this proposed architecture is to supplant convolution layers with a more sophisticated block module and to develop a compact architecture with a competitive accuracy. Further, explores the bottleneck module and squeezenext basic block structure. The state-of-the-art squeezenext baseline architecture is used as a foundation to recreate and propose a high performance squeezenext architecture. The proposed architecture is further trained on the CIFAR-10 dataset from scratch. All the training and testing results are visualized with live loss and accuracy graphs. Focus of this paper is to make an adaptable and a flexible model for efficient CNN performance which can perform better with the minimum tradeoff between model accuracy, size, and speed. Having a model size of 0.595MB along with accuracy of 92.60% and with a satisfactory training and validating speed of 9 seconds, this model can be deployed on real-time autonomous system platform such as Bluebox 2.0 by NXP.

Description
item.page.description.tableofcontents
item.page.relation.haspart
Cite As
Chappa, R. T. N. V. S., & El-Sharkawy, M. (2020). Squeeze-and-Excitation SqueezeNext: An Efficient DNN for Hardware Deployment. 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), 0691–0697. https://doi.org/10.1109/CCWC47524.2020.9031119
ISSN
Publisher
Series/Report
Sponsorship
Major
Extent
Identifier
Relation
Journal
2020 10th Annual Computing and Communication Workshop and Conference
Source
Author
Alternative Title
Type
Conference proceedings
Number
Volume
Conference Dates
Conference Host
Conference Location
Conference Name
Conference Panel
Conference Secretariat Location
Version
Author's manuscript
Full Text Available at
This item is under embargo {{howLong}}