Study of Implementation of CNN on Low-power Platform for Smart Traffic Optimization
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Accompanied with the rise of smart city and the development of IoT (Internet of Things), people are looking forward to monitoring and regulating the traffic in a smarter way. Since the deep neural network has shown its great value in vehicle detection area, people may wonder what kind of impact would be brought by the combination of IoT and deep learning techniques. In this work, an exploration of implementation of CNN (convolutional neural network) on low-power platform for smart traffic optimization has been conducted. During the research, a new optimization approach, which aims at S-CNN (Sparse Convolutional Neural Network) optimization from architecture level, has been proposed; and outstanding performance has been obtained when compared to mainstream deep learning frameworks, such as Tensorflow. In the experiments, the new proposed S-CNN optimization approach is as twice fast as Tensorflow on 94% sparse model and becomes 5 times faster on 98% sparse model. Besides, the author also verified the feasibility of real-time CNN implementation on ARM platform and Jetson TX1 embedded system, which reveals the shortage of computational resource on ARM platform and the potential of Jetson series to become the low-power platform for CNN implementation.
Cite this version of the work
Zhizhou Li (2017). Study of Implementation of CNN on Low-power Platform for Smart Traffic Optimization. UWSpace. http://hdl.handle.net/10012/12310