Browsing by Subject "SqueezeNext"

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    Design Space Exploration of DNNs for Autonomous Systems
    (2019-08) Duggal, Jayan Kant; El-Sharkawy, Mohamed; King, Brian; Rizkalla, Maher
    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.
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    High Performance SqueezeNext: Real time deployment on Bluebox 2.0 by NXP
    (ASTES, 2022-05-22) Duggal, Jayan Kant; El-Sharkawy, Mohamed; Electrical and Computer Engineering, School of Engineering and Technology
    DNN implementation and deployment is quite a challenge within a resource constrained environment on real-time embedded platforms. To attain the goal of DNN tailor made architecture deployment on a real-time embedded platform with limited hardware resources (low computational and memory resources) in comparison to a CPU or GPU based system, High Performance SqueezeNext (HPS) architecture was proposed. We propose and tailor made this architecture to be successfully deployed on Bluexbox 2.0 by NXP and also to be a DNN based on pytorch framework. High Performance SqueezeNext was inspired by SqueezeNet and SqueezeNext along with motivation derived from MobileNet architectures. High Performance SqueezeNext (HPS) achieved a model accuracy of 92.5% with 2.62MB model size at 16 seconds per epoch model using a NVIDIA based GPU system for training. It was trained and tested on various datasets such as CIFAR-10 and CIFAR-100 with no transfer learning. Thereafter, successfully deploying the proposed architecture on Bluebox 2.0, a real-time system developed by NXP with the assistance of RTMaps Remote Studio. The model accuracy results achieved were better than the existing CNN/DNN architectures model accuracies such as alexnet_tf (82% model accuracy), Maxout networks (90.65%), DCNN (89%), modified SqueezeNext (92.25%), Squeezed CNN (79.30%), MobileNet (76.7%) and an enhanced hybrid MobileNet (89.9%) with better model size. It was developed, modified and improved with the help of different optimizer implementations, hyper parameter tuning, tweaking, using no transfer learning approach and using in-place activation functions while maintaining decent accuracy.