• Title, Summary, Keyword: Convolutional Neural Network

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An Implementation of a Convolutional Accelerator based on a GPGPU for a Deep Learning (Deep Learning을 위한 GPGPU 기반 Convolution 가속기 구현)

  • Jeon, Hee-Kyeong;Lee, Kwang-yeob;Kim, Chi-yong
    • Journal of IKEEE
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    • v.20 no.3
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    • pp.303-306
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    • 2016
  • In this paper, we propose a method to accelerate convolutional neural network by utilizing a GPGPU. Convolutional neural network is a sort of the neural network learning features of images. Convolutional neural network is suitable for the image processing required to learn a lot of data such as images. The convolutional layer of the conventional CNN required a large number of multiplications and it is difficult to operate in the real-time on the embedded environment. In this paper, we reduce the number of multiplications through Winograd convolution operation and perform parallel processing of the convolution by utilizing SIMT-based GPGPU. The experiment was conducted using ModelSim and TestDrive, and the experimental results showed that the processing time was improved by about 17%, compared to the conventional convolution.

Development and Speed Comparison of Convolutional Neural Network Using CUDA (CUDA를 이용한 Convolutional Neural Network의 구현 및 속도 비교)

  • Ki, Cheol-min;Cho, Tai-Hoon
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.335-338
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    • 2017
  • Currently Artificial Inteligence and Deep Learning are social issues, and These technologies are applied to various fields. A good method among the various algorithms in Artificial Inteligence is Convolutional Neural Network. Convolutional Neural Network is a form that adds convolution layers that extracts features by convolution operation on a general neural network method. If you use Convolutional Neural Network as small amount of data, or if the structure of layers is not complicated, you don't have to pay attention to speed. But the learning time is long as the size of the learning data is large and the structure of layers is complicated. So, GPU-based parallel processing is a lot. In this paper, we developed Convolutional Neural Network using CUDA and Learning speed is faster and more efficient than the method using the CPU.

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Visualized Malware Classification Based-on Convolutional Neural Network (Convolutional Neural Network 기반의 악성코드 이미지화를 통한 패밀리 분류)

  • Seok, Seonhee;Kim, Howon
    • Journal of the Korea Institute of Information Security & Cryptology
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    • v.26 no.1
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    • pp.197-208
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    • 2016
  • In this paper, we propose a method based on a convolutional neural network which is one of the deep neural network. So, we convert a malware code to malware image and train the convolutional neural network. In experiment with classify 9-families, the proposed method records a 96.2%, 98.7% of top-1, 2 error rate. And our model can classify 27 families with 82.9%, 89% of top-1,2 error rate.

Efficient Implementation of Convolutional Neural Network Using CUDA (CUDA를 이용한 Convolutional Neural Network의 효율적인 구현)

  • Ki, Cheol-Min;Cho, Tai-Hoon
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.21 no.6
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    • pp.1143-1148
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    • 2017
  • Currently, Artificial Intelligence and Deep Learning are rising as hot social issues, and these technologies are applied to various fields. A good method among the various algorithms in Artificial Intelligence is Convolutional Neural Networks. Convolutional Neural Network is a form that adds Convolution Layers to Multi Layer Neural Network. If you use Convolutional Neural Networks for small amount of data, or if the structure of layers is not complicated, you don't have to pay attention to speed. But the learning should take long time when the size of the learning data is large and the structure of layers is complicated. In these cases, GPU-based parallel processing is frequently needed. In this paper, we developed Convolutional Neural Networks using CUDA, and show that its learning is faster and more efficient than learning using some other frameworks or programs.

Performance Comparison of Guitar Chords Classification Systems Based on Artificial Neural Network (인공신경망 기반의 기타 코드 분류 시스템 성능 비교)

  • Park, Sun Bae;Yoo, Do-Sik
    • Journal of Korea Multimedia Society
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    • v.21 no.3
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    • pp.391-399
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    • 2018
  • In this paper, we construct and compare various guitar chord classification systems using perceptron neural network and convolutional neural network without pre-processing other than Fourier transform to identify the optimal chord classification system. Conventional guitar chord classification schemes use, for better feature extraction, computationally demanding pre-processing techniques such as stochastic analysis employing a hidden markov model or an acoustic data filtering and hence are burdensome for real-time chord classifications. For this reason, we construct various perceptron neural networks and convolutional neural networks that use only Fourier tranform for data pre-processing and compare them with dataset obtained by playing an electric guitar. According to our comparison, convolutional neural networks provide optimal performance considering both chord classification acurracy and fast processing time. In particular, convolutional neural networks exhibit robust performance even when only small fraction of low frequency components of the data are used.

Performance Improvement of Object Recognition System in Broadcast Media Using Hierarchical CNN (계층적 CNN을 이용한 방송 매체 내의 객체 인식 시스템 성능향상 방안)

  • Kwon, Myung-Kyu;Yang, Hyo-Sik
    • Journal of Digital Convergence
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    • v.15 no.3
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    • pp.201-209
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    • 2017
  • This paper is a smartphone object recognition system using hierarchical convolutional neural network. The overall configuration is a method of communicating object information to the smartphone by matching the collected data by connecting the smartphone and the server and recognizing the object to the convergence neural network in the server. It is also compared to a hierarchical convolutional neural network and a fractional convolutional neural network. Hierarchical convolutional neural networks have 88% accuracy, fractional convolutional neural networks have 73% accuracy and 15%p performance improvement. Based on this, it shows possibility of expansion of T-Commerce market connected with smartphone and broadcasting media.

Wild Image Object Detection using a Pretrained Convolutional Neural Network

  • Park, Sejin;Moon, Young Shik
    • IEIE Transactions on Smart Processing and Computing
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    • v.3 no.6
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    • pp.366-371
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    • 2014
  • This paper reports a machine learning approach for image object detection. Object detection and localization in a wild image, such as a STL-10 image dataset, is very difficult to implement using the traditional computer vision method. A convolutional neural network is a good approach for such wild image object detection. This paper presents an object detection application using a convolutional neural network with pretrained feature vector. This is a very simple and well organized hierarchical object abstraction model.

Introduction to convolutional neural network using Keras; an understanding from a statistician

  • Lee, Hagyeong;Song, Jongwoo
    • Communications for Statistical Applications and Methods
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    • v.26 no.6
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    • pp.591-610
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    • 2019
  • Deep Learning is one of the machine learning methods to find features from a huge data using non-linear transformation. It is now commonly used for supervised learning in many fields. In particular, Convolutional Neural Network (CNN) is the best technique for the image classification since 2012. For users who consider deep learning models for real-world applications, Keras is a popular API for neural networks written in Python and also can be used in R. We try examine the parameter estimation procedures of Deep Neural Network and structures of CNN models from basics to advanced techniques. We also try to figure out some crucial steps in CNN that can improve image classification performance in the CIFAR10 dataset using Keras. We found that several stacks of convolutional layers and batch normalization could improve prediction performance. We also compared image classification performances with other machine learning methods, including K-Nearest Neighbors (K-NN), Random Forest, and XGBoost, in both MNIST and CIFAR10 dataset.

Efficient Fixed-Point Representation for ResNet-50 Convolutional Neural Network (ResNet-50 합성곱 신경망을 위한 고정 소수점 표현 방법)

  • Kang, Hyeong-Ju
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.22 no.1
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    • pp.1-8
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    • 2018
  • Recently, the convolutional neural network shows high performance in many computer vision tasks. However, convolutional neural networks require enormous amount of operation, so it is difficult to adopt them in the embedded environments. To solve this problem, many studies are performed on the ASIC or FPGA implementation, where an efficient representation method is required. The fixed-point representation is adequate for the ASIC or FPGA implementation but causes a performance degradation. This paper proposes a separate optimization of representations for the convolutional layers and the batch normalization layers. With the proposed method, the required bit width for the convolutional layers is reduced from 16 bits to 10 bits for the ResNet-50 neural network. Since the computation amount of the convolutional layers occupies the most of the entire computation, the bit width reduction in the convolutional layers enables the efficient implementation of the convolutional neural networks.

Snoring identification method based on residual convolutional neural network (잔류 합성 곱 신경망 기반의 코골이 식별 방식)

  • Shin, Seung-Su;Kim, Hyoung-Gook
    • The Journal of the Acoustical Society of Korea
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    • v.38 no.5
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    • pp.574-579
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    • 2019
  • Snoring is a typical symptom of sleep disorder and it is important to identify the occurrence of snoring because it causes sleep apnea. In this paper, we proposes a residual convolutional neural network as an efficient snoring identification algorithm. Residual convolutional neural network, which is a structure combining residual learning and convolutional neural network, effectively extracts features existing in data more than conventional neural network and improves the accuracy of snoring identification. Experimental results show that the performance of the proposed snoring algorithm is superior to that of the conventional methods.