DESIGN SPACE EXPLORATION OF DNNS FOR AUTONOMOUS SYSTEMS
thesisposted on 16.10.2019 by Jayan Kant Duggal
In order to distinguish essays and pre-prints from academic theses, we have a separate category. These are often much longer text based documents than a paper.
Developing intelligent agents that can perceive and understand the rich visual world around us has been a long-standing goal in the field of AI. Recently, a significant 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 and other techniques have made CNN/ DNN memory & computationally efficient. But the major design hurdles for deployment are limited resources such as computation, memory, energy efficiency, and power budget. DSE of small DNN architectures for ADAS emerged with better and efficient architectures such as baseline SqueezeNet and SqueezeNext. These architectures are exclusively known for their small model size, good model speed & model accuracy. In this thesis study, two new DNN architectures are proposed. Before diving into the proposed architectures, DSE of DNNs explores the methods to improve DNNs/ CNNs. Further, understanding the different hyperparameters tuning & experimenting with various optimizers and newly introduced methodologies. First, High Performance SqueezeNext architecture ameliorate the performance of existing DNN architectures.The intuition behind this proposed architecture is to supplant convolution layers with a more sophisticated block module & to develop a compact and efficient architecture with a competitive accuracy. Second, Shallow SqueezeNext architecture is proposed which achieves better model size results in comparison to baseline SqueezeNet and SqueezeNext is presented. It illustrates the architecture is compact, efficient and flexible in terms of model size and accuracy. The state-of-the-art SqueezeNext baseline and SqueezeNext baseline are used as the foundation to recreate and propose the both DNN architectures in this study. Due to very small model size with competitive model accuracy and decent model testing speed it is expected to perform well on the ADAS systems. The proposed architectures are trained and tested from scratch on CIFAR-10 & CIFAR-100 datasets. All the training and testing results are visualized with live loss and accuracy graphs by using livelossplot. In the last, both of the proposed DNN architectures are deployed on BlueBox2.0 by NXP.