Indiana University-Purdue University Indianapolis (IUPUI) / In recent years, Convolutional Neural Networks (CNNs) have become the state-of-
the-art method for object detection and classi cation in the eld of machine learning
and arti cial intelligence. In contrast to a fully connected network, each neuron of a
convolutional layer of a CNN is connected to fewer selected neurons from the previous
layers and kernels of a CNN share same weights and biases across the same input layer
dimension. These features allow CNN architectures to have fewer parameters which in
turn reduces calculation complexity and allows the network to be implemented in low
power hardware. The accuracy of a CNN depends mostly on the number of images
used to train the network, which requires a hundred thousand to a million images.
Therefore, a reduced training alternative called transfer learning is used, which takes
advantage of features from a pre-trained network and applies these features to the new
problem of interest. This research has successfully developed a new CNN based on
the pre-trained CIFAR-10 network and has used transfer learning on a new problem
to classify road edges. Two network sizes were tested: 32 and 16 Neuron inputs with
239 labeled Google street view images on a single CPU. The result of the training
gives 52.8% and 35.2% accuracy respectively for 250 test images. In the second part
of the research, High Level Synthesis (HLS) hardware model of the network with 16
Neuron inputs is created for the Zynq 7000 FPGA. The resulting circuit has 34%
average FPGA utilization and 2.47 Watt power consumption. Recommendations to
improve the classi cation accuracy with deeper network and ways to t the improved
network on the FPGA are also mentioned at the end of the work.
| Identifer | oai:union.ndltd.org:IUPUI/oai:scholarworks.iupui.edu:1805/15113 |
| Date | 12 1900 |
| Creators | Rahman, Tanvir |
| Contributors | Christopher, Lauren |
| Source Sets | Indiana University-Purdue University Indianapolis |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Rights | Attribution 3.0 United States, http://creativecommons.org/licenses/by/3.0/us/ |
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