Browsing by Subject "Bluebox 2.0"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Compressed convolutional neural network for autonomous systems
    (2018-12) Pathak, Durvesh; El-Sharkawy, Mohamed; Rizkalla, Maher; King, Brian
    The word “Perception” seems to be intuitive and maybe the most straightforward problem for the human brain because as a child we have been trained to classify images, detect objects, but for computers, it can be a daunting task. Giving intuition and reasoning to a computer which has mere capabilities to accept commands and process those commands is a big challenge. However, recent leaps in hardware development, sophisticated software frameworks, and mathematical techniques have made it a little less daunting if not easy. There are various applications built around to the concept of “Perception”. These applications require substantial computational resources, expensive hardware, and some sophisticated software frameworks. Building an application for perception for the embedded system is an entirely different ballgame. Embedded system is a culmination of hardware, software and peripherals developed for specific tasks with imposed constraints on memory and power. Therefore, the applications developed should keep in mind the memory and power constraints imposed due to the nature of these systems. Before 2012, the problems related to “Perception” such as classification, object detection were solved using algorithms with manually engineered features. However, in recent years, instead of manually engineering the features, these features are learned through learning algorithms. The game-changing architecture of Convolution Neural Networks proposed in 2012 by Alex K [1], provided a tremendous momentum in the direction of pushing Neural networks for perception. This thesis is an attempt to develop a convolution neural network architecture for embedded systems, i.e. an architecture that has a small model size and competitive accuracy. Recreate state-of-the-art architectures using fire module’s concept to reduce the model size of the architecture. The proposed compact models are feasible for deployment on embedded devices such as the Bluebox 2.0. Furthermore, attempts are made to integrate the compact Convolution Neural Network with object detection pipelines.
  • Thumbnail Image
    Item
    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.