Latest revision as of 20:53, 28 January 2021

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.

Original document

The different versions of the original document can be found in:

• https://scholarworks.iupui.edu/bitstream/1805/22611/1/Chappa_Thesis-10.pdf under the license https://creativecommons.org/licenses/by-nd

• http://xplorestaging.ieee.org/ielx7/9017208/9031108/09031119.pdf?arnumber=9031119,

http://dx.doi.org/10.1109/ccwc47524.2020.9031119 under the license cc-by-nd

• https://scholarworks.iupui.edu/handle/1805/22611,

https://dblp.uni-trier.de/db/conf/ccwc/ccwc2020.html#ChappaE20,

https://hammer.figshare.com/articles/Squeeze-and-Excitation_SqueezeNext_An_Efficient_DNN_for_Hardware_Deployment/12170394,

https://academic.microsoft.com/#/detail/3011941104