• Title, Summary, Keyword: 컨볼루션 연산

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A Pipelined Parallel Optimized Design for Convolution-based Non-Cascaded Architecture of JPEG2000 DWT (JPEG2000 이산웨이블릿변환의 컨볼루션기반 non-cascaded 아키텍처를 위한 pipelined parallel 최적화 설계)

  • Lee, Seung-Kwon;Kong, Jin-Hyeung
    • Journal of the Institute of Electronics Engineers of Korea SD
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    • v.46 no.7
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    • pp.29-38
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    • 2009
  • In this paper, a high performance pipelined computing design of parallel multiplier-temporal buffer-parallel accumulator is present for the convolution-based non-cascaded architecture aiming at the real time Discrete Wavelet Transform(DWT) processing. The convolved multiplication of DWT would be reduced upto 1/4 by utilizing the filter coefficients symmetry and the up/down sampling; and it could be dealt with 3-5 times faster computation by LUT-based DA multiplication of multiple filter coefficients parallelized for product terms with an image data. Further, the reutilization of computed product terms could be achieved by storing in the temporal buffer, which yields the saving of computation as well as dynamic power by 50%. The convolved product terms of image data and filter coefficients are realigned and stored in the temporal buffer for the accumulated addition. Then, the buffer management of parallel aligned storage is carried out for the high speed sequential retrieval of parallel accumulations. The convolved computation is pipelined with parallel multiplier-temporal buffer-parallel accumulation in which the parallelization of temporal buffer and accumulator is optimize, with respect to the performance of parallel DA multiplier, to improve the pipelining performance. The proposed architecture is back-end designed with 0.18um library, which verifies the 30fps throughput of SVGA(800$\times$600) images at 90MHz.

Trajectory Generation Method with Convolution Operation on Velocity Profile (속도 영역에서의 컨볼루션을 이용한 효율적인 궤적 생성 방법)

  • Lee, Geon;Kim, Doik
    • Transactions of the Korean Society of Mechanical Engineers A
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    • v.38 no.3
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    • pp.283-288
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    • 2014
  • The use of robots is no longer limited to the field of industrial robots and is now expanding into the fields of service and medical robots. In this light, a trajectory generation method that can respond instantaneously to the external environment is strongly required. Toward this end, this study proposes a method that enables a robot to change its trajectory in real-time using a convolution operation. The proposed method generates a trajectory in real time and satisfies the physical limits of the robot system such as acceleration and velocity limit. Moreover, a new way to improve the previous method (11), which generates inefficient trajectories in some cases owing to the characteristics of the trapezoidal shape of trajectories, is proposed by introducing a triangle shape. The validity and effectiveness of the proposed method is shown through a numerical simulation and a comparison with the previous convolution method.

Implementation of MNIST classification CNN with zero-skipping (Zero-skipping을 적용한 MNIST 분류 CNN 구현)

  • Han, Seong-hyeon;Jung, Jun-mo
    • Journal of IKEEE
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    • v.22 no.4
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    • pp.1238-1241
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    • 2018
  • In this paper, MNIST classification CNN with zero skipping is implemented. Activation of CNN results in 30% to 40% zero. Since 0 does not affect the MAC operation, skipping 0 through a branch can improve performance. However, at the convolution layer, skipping over a branch causes a performance degradation. Accordingly, in the convolution layer, an operation is skipped by giving a NOP that does not affect the operation. Fully connected layer is skipped through the branch. We have seen performance improvements of about 1.5 times that of existing CNN.

A Study on Random Selection of Pooling Operations for Regularization and Reduction of Cross Validation (정규화 및 교차검증 횟수 감소를 위한 무작위 풀링 연산 선택에 관한 연구)

  • Ryu, Seo-Hyeon
    • Journal of the Korea Academia-Industrial cooperation Society
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    • v.19 no.4
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    • pp.161-166
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    • 2018
  • In this paper, we propose a method for the random selection of pooling operations for the regularization and reduction of cross validation in convolutional neural networks. The pooling operation in convolutional neural networks is used to reduce the size of the feature map and for its shift invariant properties. In the existing pooling method, one pooling operation is applied in each pooling layer. Because this method fixes the convolution network, the network suffers from overfitting, which means that it excessively fits the models to the training samples. In addition, to find the best combination of pooling operations to maximize the performance, cross validation must be performed. To solve these problems, we introduce the probability concept into the pooling layers. The proposed method does not select one pooling operation in each pooling layer. Instead, we randomly select one pooling operation among multiple pooling operations in each pooling region during training, and for testing purposes, we use probabilistic weighting to produce the expected output. The proposed method can be seen as a technique in which many networks are approximately averaged using a different pooling operation in each pooling region. Therefore, this method avoids the overfitting problem, as well as reducing the amount of cross validation. The experimental results show that the proposed method can achieve better generalization performance and reduce the need for cross validation.

Binary CNN Operation Algorithm using Bit-plane Image (비트평면 영상을 이용한 이진 CNN 연산 알고리즘)

  • Choi, Jong-Ho
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.12 no.6
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    • pp.567-572
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    • 2019
  • In this paper, we propose an algorithm to perform convolution, pooling, and ReLU operations in CNN using binary image and binary kernel. It decomposes 256 gray-scale images into 8 bit planes and uses a binary kernel consisting of -1 and 1. The convolution operation of binary image and binary kernel is performed by addition and subtraction. Logically, it is a binary operation algorithm using the XNOR and comparator. ReLU and pooling operations are performed by using XNOR and OR logic operations, respectively. Through the experiments to verify the usefulness of the proposed algorithm, We confirm that the CNN operation can be performed by converting it to binary logic operation. It is an algorithm that can implement deep running even in a system with weak computing power. It can be applied to a variety of embedded systems such as smart phones, intelligent CCTV, IoT system, and autonomous car.

Parallel-Addition Convolution Algorithm in Grayscale Image (그레이스케일 영상의 병렬가산 컨볼루션 알고리즘)

  • Choi, Jong-Ho
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.10 no.4
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    • pp.288-294
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    • 2017
  • Recently, deep learning using convolutional neural network (CNN) has been extensively studied in image recognition. Convolution consists of addition and multiplication. Multiplication is computationally expensive in hardware implementation, relative to addition. It is also important factor limiting a chip design in an embedded deep learning system. In this paper, I propose a parallel-addition processing algorithm that converts grayscale images to the superposition of binary images and performs convolution only with addition. It is confirmed that the convolution can be performed by a parallel-addition method capable of reducing the processing time in experiment for verifying the availability of proposed algorithm.

Reduction Method of Computational Complexity for Image Filtering Utilizing the Factorization Theorem (인수분해 공식을 이용한 영상 필터링 연산량 저감 방법)

  • Jung, Chan-sung;Lee, Jaesung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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    • pp.354-357
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    • 2013
  • The filtering algorithm is used very frequently in the preprocessing stage of many image processing algorithms in computer vision processing. Because video signals are two-dimensional signals, computaional complexity is very high. To reduce the complexity, separable filters and the factorization theorem is applied to the filtering operation. As a result, it is shown that a significant reduction in computational complexity is achieved, although the experimental results could be slightly different depending on the condition of the image.

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A Deep Learning-Based Face Mesh Data Denoising System (딥 러닝 기반 얼굴 메쉬 데이터 디노이징 시스템)

  • Roh, Jihyun;Im, Hyeonseung;Kim, Jongmin
    • Journal of IKEEE
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    • v.23 no.4
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    • pp.1250-1256
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    • 2019
  • Although one can easily generate real-world 3D mesh data using a 3D printer or a depth camera, the generated data inevitably includes unnecessary noise. Therefore, mesh denoising is essential to obtain intact 3D mesh data. However, conventional mathematical denoising methods require preprocessing and often eliminate some important features of the 3D mesh. To address this problem, this paper proposes a deep learning based 3D mesh denoising method. Specifically, we propose a convolution-based autoencoder model consisting of an encoder and a decoder. The convolution operation applied to the mesh data performs denoising considering the relationship between each vertex constituting the mesh data and the surrounding vertices. When the convolution is completed, a sampling operation is performed to improve the learning speed. Experimental results show that the proposed autoencoder model produces faster and higher quality denoised data than the conventional methods.

Improved Multi-modal Network Using Dilated Convolution Pyramid Pooling (팽창된 합성곱 계층 연산 풀링을 이용한 멀티 모달 네트워크 성능 향상 방법)

  • Park, Jun-Young;Ho, Yo-Sung
    • Proceedings of the Korean Society of Broadcast Engineers Conference
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    • pp.84-86
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    • 2018
  • 요즘 자율주행과 같은 최신 기술의 발전과 더불어 촬영된 영상 장면에 대한 깊이있는 이해가 필요하게 되었다. 특히, 기계학습 기술이 발전하면서 카메라로 찍은 영상에 대한 의미론적 분할 기술에 대한 연구도 활발히 진행되고 있다. FuseNet은 인코더-디코더 구조를 이용하여 장면 내에 있는 객체에 대한 의미론적 분할 기술을 적용할 수 있는 신경망 모델이다. FuseNet은 오직 RGB 입력을 받는 기존의 FCN보다 깊이정보까지 활용하여 RGB 정보를 기반으로 추출한 특징지도와의 요소합 연산을 통해 멀티 모달 구조를 구현했다. 의미론적 분할 연구에서는 객체의 전역 컨텍스트가 고려되는 것이 중요한데, 이를 위해 여러 계층을 깊게 쌓으면 연산량이 많아지는 단점이 있다. 이를 극복하기 위해서 기존의 합성곱 방식을 벗어나 새롭게 제안된 팽창 합성곱 연산(Dilated Convolution)을 이용하면 객체의 수용 영역이 효과적으로 넓어지고 연산량이 적어질 수 있다. 본 논문에서는 컨볼루션 연산의 새로운 방법론적 접근 중 하나인 팽창된 합성곱 연산을 이용해 의미론적 분할 연구에서 새로운 멀티 모달 네트워크의 성능 향상 방법을 적용하여 계층을 더 깊게 쌓지 않더라도 파라미터의 증가 없이 해상도를 유지하면서 네트워크의 전체 성능을 향상할 수 있는 최적화된 방법을 제안한다.

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