Browsing by Subject "convolutional neural nets"

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    Deployment of SE-SqueezeNext on NXP BlueBox 2.0 and NXP i.MX RT1060 MCU
    (IEEE, 2020-08) Chappa, Ravi Teja N. V. S.; El-Sharkawy, Mohamed; Electrical and Computer Engineering, School of Engineering and Technology
    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.