• Title, Summary, Keyword: Convolution neural network

Search Result 222, Processing Time 0.046 seconds

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
    • /
    • v.20 no.3
    • /
    • pp.303-306
    • /
    • 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.

Network Packet Classification Using Convolution Neural Network and Recurrent Neural Network (Convolution Neural Network와 Recurrent Neural Network를 활용한 네트워크 패킷 분류)

  • Lim, Hyun-Kyo;Kim, Ju-Bong;Han, Youn-Hee
    • Proceedings of the Korea Information Processing Society Conference
    • /
    • /
    • pp.16-18
    • /
    • 2018
  • 최근 네트워크 상에 새롭고 다양한 어플리케이션들이 생겨나면서 이에 따른 적절한 어플리케이션별 서비스 제공을 위한 패킷 분류 방법이 요구되고 있다. 이로 인하여 딥 러닝 기술이 발전 하면서 이를 이용한 네트워크 트래픽 분류 방법들이 제안되고 있다. 따라서, 본 논문에서는 딥 러닝 기술 중 Convolution Neural Network 와 Recurrent Neural Network 를 동시에 활용한 네트워크 패킷 분류 방법을 제안한다.

Neural Network Image Reconstruction for Magnetic Particle Imaging

  • Chae, Byung Gyu
    • ETRI Journal
    • /
    • v.39 no.6
    • /
    • pp.841-850
    • /
    • 2017
  • We investigate neural network image reconstruction for magnetic particle imaging. The network performance strongly depends on the convolution effects of the spectrum input data. The larger convolution effect appearing at a relatively smaller nanoparticle size obstructs the network training. The trained single-layer network reveals the weighting matrix consisting of a basis vector in the form of Chebyshev polynomials of the second kind. The weighting matrix corresponds to an inverse system matrix, where an incoherency of basis vectors due to low convolution effects, as well as a nonlinear activation function, plays a key role in retrieving the matrix elements. Test images are well reconstructed through trained networks having an inverse kernel matrix. We also confirm that a multi-layer network with one hidden layer improves the performance. Based on the results, a neural network architecture overcoming the low incoherence of the inverse kernel through the classification property is expected to become a better tool for image reconstruction.

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
    • /
    • /
    • pp.335-338
    • /
    • 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.

  • PDF

VLSI Design of High Speed Digital Neural Network using the Binary Convolution (Binar Convolution을 이용한 고속 디지탈 신경회로망의 VLSI 설계)

  • Choi, Seung-Ho;Kim, Young-Min
    • The Journal of the Acoustical Society of Korea
    • /
    • v.15 no.5
    • /
    • pp.13-20
    • /
    • 1996
  • Recently, for implementation of neural networks extensive studies have been done especially VLSI technology has been regarded as the one of the most attractive means to implement neural networks. The main drawbacks of digital VLSI implementations are their large area and slow processing speed. In this paper to solve the speed and size problems we designed the efficient architecture using the binary convolution method for basic operation of neural cell, multiplication and addition. When it is used for implementing 3-layer network with 16 neural cell per layer that used neural cell based on binary convolution, clock of 50MHz and 26MCPS on 0.8${\mu}$ standard cell library has been achieved.

  • PDF

Network Traffic Classification Based on Deep Learning

  • Li, Junwei;Pan, Zhisong
    • KSII Transactions on Internet and Information Systems (TIIS)
    • /
    • v.14 no.11
    • /
    • pp.4246-4267
    • /
    • 2020
  • As the network goes deep into all aspects of people's lives, the number and the complexity of network traffic is increasing, and traffic classification becomes more and more important. How to classify them effectively is an important prerequisite for network management and planning, and ensuring network security. With the continuous development of deep learning, more and more traffic classification begins to use it as the main method, which achieves better results than traditional classification methods. In this paper, we provide a comprehensive review of network traffic classification based on deep learning. Firstly, we introduce the research background and progress of network traffic classification. Then, we summarize and compare traffic classification based on deep learning such as stack autoencoder, one-dimensional convolution neural network, two-dimensional convolution neural network, three-dimensional convolution neural network, long short-term memory network and Deep Belief Networks. In addition, we compare traffic classification based on deep learning with other methods such as based on port number, deep packets detection and machine learning. Finally, the future research directions of network traffic classification based on deep learning are prospected.

Human Action Recognition Based on 3D Convolutional Neural Network from Hybrid Feature

  • Wu, Tingting;Lee, Eung-Joo
    • Journal of Korea Multimedia Society
    • /
    • v.22 no.12
    • /
    • pp.1457-1465
    • /
    • 2019
  • 3D convolution is to stack multiple consecutive frames to form a cube, and then apply the 3D convolution kernel in the cube. In this structure, each feature map of the convolutional layer is connected to multiple adjacent sequential frames in the previous layer, thus capturing the motion information. However, due to the changes of pedestrian posture, motion and position, the convolution at the same place is inappropriate, and when the 3D convolution kernel is convoluted in the time domain, only time domain features of three consecutive frames can be extracted, which is not a good enough to get action information. This paper proposes an action recognition method based on feature fusion of 3D convolutional neural network. Based on the VGG16 network model, sending a pre-acquired optical flow image for learning, then get the time domain features, and then the feature of the time domain is extracted from the features extracted by the 3D convolutional neural network. Finally, the behavior classification is done by the SVM classifier.

New Approach to Optimize the Size of Convolution Mask in Convolutional Neural Networks

  • Kwak, Young-Tae
    • Journal of the Korea Society of Computer and Information
    • /
    • v.21 no.1
    • /
    • pp.1-8
    • /
    • 2016
  • Convolutional neural network (CNN) consists of a few pairs of both convolution layer and subsampling layer. Thus it has more hidden layers than multi-layer perceptron. With the increased layers, the size of convolution mask ultimately determines the total number of weights in CNN because the mask is shared among input images. It also is an important learning factor which makes or breaks CNN's learning. Therefore, this paper proposes the best method to choose the convolution size and the number of layers for learning CNN successfully. Through our face recognition with vast learning examples, we found that the best size of convolution mask is 5 by 5 and 7 by 7, regardless of the number of layers. In addition, the CNN with two pairs of both convolution and subsampling layer is found to make the best performance as if the multi-layer perceptron having two hidden layers does.

Implementation to eye motion tracking system using convolutional neural network (Convolutional neural network를 이용한 눈동자 모션인식 시스템 구현)

  • Lee, Seung Jun;Heo, Seung Won;Lee, Hee Bin;Yu, Yun Seop
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • /
    • pp.703-704
    • /
    • 2018
  • An artificial neural network design that traces the pupil for the disables suffering from Lou Gehrig disease is introduced. It grasps the position of the pupil required for the communication system. Tensorflow is used for generating and learning the neural network, and the pupil position is determined through the learned neural network. Convolution neural network(CNN) which consists of 2 stages of convolution layer and 2 layers of complete connection layer is implemented for the system.

  • PDF

A Stock Price Prediction Based on Recurrent Convolution Neural Network with Weighted Loss Function (가중치 손실 함수를 가지는 순환 컨볼루션 신경망 기반 주가 예측)

  • Kim, HyunJin;Jung, Yeon Sung
    • KIPS Transactions on Software and Data Engineering
    • /
    • v.8 no.3
    • /
    • pp.123-128
    • /
    • 2019
  • This paper proposes the stock price prediction based on the artificial intelligence, where the model with recurrent convolution neural network (RCNN) layers is adopted. In the motivation of this prediction, long short-term memory model (LSTM)-based neural network can make the output of the time series prediction. On the other hand, the convolution neural network provides the data filtering, averaging, and augmentation. By combining the advantages mentioned above, the proposed technique predicts the estimated stock price of next day. In addition, in order to emphasize the recent time series, a custom weighted loss function is adopted. Moreover, stock data related to the stock price index are adopted to consider the market trends. In the experiments, the proposed stock price prediction reduces the test error by 3.19%, which is over other techniques by about 19%.