• 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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    • 2017.05a
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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.

Twowheeled Motor Vehicle License Plate Recognition Algorithm using CPU based Deep Learning Convolutional Neural Network (CPU 기반의 딥러닝 컨볼루션 신경망을 이용한 이륜 차량 번호판 인식 알고리즘)

  • Kim Jinho
    • Journal of Korea Society of Digital Industry and Information Management
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    • v.19 no.4
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    • pp.127-136
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    • 2023
  • Many research results on the traffic enforcement of illegal driving of twowheeled motor vehicles using license plate recognition are introduced. Deep learning convolutional neural networks can be used for character and word recognition of license plates because of better generalization capability compared to traditional Backpropagation neural networks. In the plates of twowheeled motor vehicles, the interdependent government and city words are included. If we implement the mutually independent word recognizers using error correction rules for two word recognition results, efficient license plate recognition results can be derived. The CPU based convolutional neural network without library under real time processing has an advantage of low cost real application compared to GPU based convolutional neural network with library. In this paper twowheeled motor vehicle license plate recognition algorithm is introduced using CPU based deep-learning convolutional neural network. The experimental results show that the proposed plate recognizer has 96.2% success rate for outdoor twowheeled motor vehicle images in real time.

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.

Development of Convolutional Neural Network Basic Practice Cases (합성곱 신경망 기초 실습 사례 개발)

  • Hur, Kyeong
    • Journal of Practical Engineering Education
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    • v.14 no.2
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    • pp.279-285
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    • 2022
  • In this paper, as a liberal arts course for non-majors, we developed a basic practice case for convolutional neural networks, which is essential for designing a basic convolutional neural network course curriculum. The developed practice case focuses on understanding the working principle of the convolutional neural network and uses a spreadsheet to check the entire visualized process. The developed practice case consisted of generating supervised learning method image training data, implementing the input layer, convolution layer (convolutional layer), pooling layer, and output layer sequentially, and testing the performance of the convolutional neural network on new data. By extending the practice cases developed in this paper, the number of images to be recognized can be expanded, or basic practice cases can be made to create a convolutional neural network that increases the compression rate for high-quality images. Therefore, it can be said that the utility of this convolutional neural network basic practice case is high.

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.

Graph Convolutional - Network Architecture Search : Network architecture search Using Graph Convolution Neural Networks (그래프 합성곱-신경망 구조 탐색 : 그래프 합성곱 신경망을 이용한 신경망 구조 탐색)

  • Su-Youn Choi;Jong-Youel Park
    • The Journal of the Convergence on Culture Technology
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    • v.9 no.1
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    • pp.649-654
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    • 2023
  • This paper proposes the design of a neural network structure search model using graph convolutional neural networks. Deep learning has a problem of not being able to verify whether the designed model has a structure with optimized performance due to the nature of learning as a black box. The neural network structure search model is composed of a recurrent neural network that creates a model and a convolutional neural network that is the generated network. Conventional neural network structure search models use recurrent neural networks, but in this paper, we propose GC-NAS, which uses graph convolutional neural networks instead of recurrent neural networks to create convolutional neural network models. The proposed GC-NAS uses the Layer Extraction Block to explore depth, and the Hyper Parameter Prediction Block to explore spatial and temporal information (hyper parameters) based on depth information in parallel. Therefore, since the depth information is reflected, the search area is wider, and the purpose of the search area of the model is clear by conducting a parallel search with depth information, so it is judged to be superior in theoretical structure compared to GC-NAS. GC-NAS is expected to solve the problem of the high-dimensional time axis and the range of spatial search of recurrent neural networks in the existing neural network structure search model through the graph convolutional neural network block and graph generation algorithm. In addition, we hope that the GC-NAS proposed in this paper will serve as an opportunity for active research on the application of graph convolutional neural networks to neural network structure search.