Squeeze-and-Excitation SqueezeNext: An Efficient DNN for Hardware Deployment

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

Accurate simulation of heating properties of Mettler 27 MHz diathermy system using finite element methods

Project (M.S., Electrical and Electronic Engineering) -- California State University, Sacramento, 2010.This project will focus on the practical simulation of the focusing properties of the 27 MHz Mettler diathermy system that is potentially used as a heating tool in the treatment of tumors by hyperthermia. The simulation is carried out using the advanced 4NEC2 Finite Element Method software. The goal is the accurately model the focusing properties of the diathermy system with a view to improve the efficiency of the system by possibly using conductive reflectors to enhance the focusing property. In this project, it was possible to accurately model the diathermy system, and generate the 3-dimensional electric field patterns of the system. These results will be compared with experimental results that are being generated presently as part of another student project.Electrical and Electronic Engineerin

Deployment of SE-SqueezeNext on NXP BlueBox 2.0 and NXP i.MX RT1060 MCU

Convolution neural system is being utilized in field of self-governing driving vehicles or driver assistance systems (ADAS), and has made extraordinary progress. Before the CNN, conventional AI calculations helped ADAS. Right now, there is an incredible investigation being done in DNNs like MobileNet, SqueezeNext & SqueezeNet. It improved the CNN designs and made it increasingly appropriate to actualize on real-time embedded systems. Due to the model size complexity of many models, they cannot be deployed straight away on real-time systems. The most important requirement will be to have less model size without a tradeoff with accuracy. Squeeze-and-Excitation SqueezeNext which is an efficient DNN with best model accuracy of 92.60% and with least model size of 0.595MB is chosen to be deployed on NXP BlueBox 2.0 and NXP i.MX RT1060. This deployment is very successful because of its less size and better accuracy. The model is trained and validated on CIFAR-10 dataset