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http://dx.doi.org/10.7471/ikeee.2016.20.4.351

A Real-Time Hardware Design of CNN for Vehicle Detection  

Bang, Ji-Won (Dept. of Electronics & Communications Engineering, KwangWoon University)
Jeong, Yong-Jin (Dept. of Electronics & Communications Engineering, KwangWoon University)
Publication Information
Journal of IKEEE / v.20, no.4, 2016 , pp. 351-360 More about this Journal
Abstract
Recently, machine learning algorithms, especially deep learning-based algorithms, have been receiving attention due to its high classification performance. Among the algorithms, Convolutional Neural Network(CNN) is known to be efficient for image processing tasks used for Advanced Driver Assistance Systems(ADAS). However, it is difficult to achieve real-time processing for CNN in vehicle embedded software environment due to the repeated operations contained in each layer of CNN. In this paper, we propose a hardware accelerator which enhances the execution time of CNN by parallelizing the repeated operations such as convolution. Xilinx ZC706 evaluation board is used to verify the performance of the proposed accelerator. For $36{\times}36$ input images, the hardware execution time of CNN is 2.812ms in 100MHz clock frequency and shows that our hardware can be executed in real-time.
Keywords
Convolutional Neural Network; ADAS; FPGA; Vehicle Detection; Mechine Learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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