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IUPUI

1. Duggal, Jayan Kant. Design Space Exploration of DNNs for Autonomous Systems.

Degree: 2019, IUPUI

Indiana University-Purdue University Indianapolis (IUPUI)

Developing intelligent agents that can perceive and understand the rich visualworld around us has been a long-standing goal in the field of AI. Recently, asignificant progress has been made by the CNNs/DNNs to the incredible advances& in a wide range of applications such as ADAS, intelligent cameras surveillance,autonomous systems, drones, & robots. Design space exploration (DSE) of NNs andother techniques have made CNN/DNN memory & computationally efficient. Butthe major design hurdles for deployment are limited resources such as computation,memory, energy efficiency, and power budget. DSE of small DNN architectures forADAS emerged with better and efficient architectures such as baseline SqueezeNetand SqueezeNext. These architectures are exclusively known for their small modelsize, good model speed & model accuracy.In this thesis study, two new DNN architectures are proposed. Before diving intothe proposed architectures, DSE of DNNs explores the methods to improveDNNs/CNNs.Further, understanding the different hyperparameters tuning &experimenting with various optimizers and newly introduced methodologies. First,High Performance SqueezeNext architecture ameliorate the performance of existingDNN architectures. The intuition behind this proposed architecture is to supplantconvolution layers with a more sophisticated block module & to develop a compactand efficient architecture with a competitive accuracy. Second, Shallow SqueezeNextarchitecture is proposed which achieves better model size results in comparison tobaseline SqueezeNet and SqueezeNext is presented. It illustrates the architecture is xviicompact, efficient and flexible in terms of model size and accuracy.Thestate-of-the-art SqueezeNext baseline and SqueezeNext baseline are used as thefoundation to recreate and propose the both DNN architectures in this study. Dueto very small model size with competitive model accuracy and decent model testingspeed it is expected to perform well on the ADAS systems.The proposedarchitectures are trained and tested from scratch on CIFAR-10 [30] & CIFAR-100[34] datasets. All the training and testing results are visualized with live loss andaccuracy graphs by using livelossplot. In the last, both of the proposed DNNarchitectures are deployed on BlueBox2.0 by NXP.

Advisors/Committee Members: El-Sharkawy, Mohamed, King, Brian, Rizkalla, Maher.

Subjects/Keywords: DNNs; CNNs; DSE of DNNs; Image Classification; SqueezeNext; BlueBox

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Duggal, J. K. (2019). Design Space Exploration of DNNs for Autonomous Systems. (Thesis). IUPUI. Retrieved from http://hdl.handle.net/1805/19924

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Duggal, Jayan Kant. “Design Space Exploration of DNNs for Autonomous Systems.” 2019. Thesis, IUPUI. Accessed August 24, 2019. http://hdl.handle.net/1805/19924.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Duggal, Jayan Kant. “Design Space Exploration of DNNs for Autonomous Systems.” 2019. Web. 24 Aug 2019.

Vancouver:

Duggal JK. Design Space Exploration of DNNs for Autonomous Systems. [Internet] [Thesis]. IUPUI; 2019. [cited 2019 Aug 24]. Available from: http://hdl.handle.net/1805/19924.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Duggal JK. Design Space Exploration of DNNs for Autonomous Systems. [Thesis]. IUPUI; 2019. Available from: http://hdl.handle.net/1805/19924

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

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