CNN Based High Performance Computing for Real Time Image Processing on GPU

Potluri, Sasanka; Fasih, Alireza; Vutukuru, Laxminand Kishore; Machot, Fadi Al; Kyamakya, Kyandoghere

doi:10.1007/978-3-642-24806-1_20

Cited by 18 publications

(18 citation statements)

References 5 publications

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“…LeNet is a feed-forward NN that constitutes of five alternating layers of convolutional and pooling, followed by two fully connected layers. In early 2000, GPU was not commonly used to speed up training, and even CPUs were slow [135]. The main limitation of traditional multilayered fully connected NN was that it considers each pixel as a separate input and applies a transformation on it, which was a huge computational burden, specifically at that time [136].…”

Section: Lenetmentioning

confidence: 99%

A survey of the recent architectures of deep convolutional neural networks

et al. 2020

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Deep Convolutional Neural Networks (CNNs) are a special type of Neural Networks, which have shown exemplary performance on several competitions related to Computer Vision and Image Processing. Interesting application areas of CNN include Image Classification and Segmentation, Object Detection, Video Processing, Natural Language Processing, Speech Recognition, etc. The powerful learning ability of deep CNN is largely due to the use of multiple feature extraction stages that can automatically learn representations from the data. Availability of a large amount of data and improvements in the hardware technology have accelerated the research in CNNs, and recently very interesting deep CNN architectures have been reported. In fact, several interesting ideas to bring advancements in CNNs have been explored such as the use of different activation and loss functions, parameter optimization, regularization, and architectural innovations. However, the major improvement in representational capacity of the deep CNN is achieved through architectural innovations. Especially, the idea of exploiting spatial and channel information, depth and width of architecture, and multi-path information processing has gained substantial attention. Similarly, the idea of using a block of layers as a structural unit is also gaining popularity. This survey thus focuses on the intrinsic taxonomy present in the recently reported deep CNN architectures and consequently, classifies the recent innovations in CNN architectures into seven different categories. These seven categories are based on spatial exploitation, depth, multi-path, width, feature-map exploitation, channel boosting, and attention. Additionally, the elementary understanding of CNN components, current challenges and applications of CNN are also provided. CNNs are the best among learning algorithms in understanding images content, and have shown exemplary results in segmentation, classification, detection, and retrieval related tasks [8], [9]. The success of CNNs has captured attention beyond academia. In industry, companies such as Google, Microsoft, AT&T, NEC, and Facebook have developed active research groups for exploring new architectures of CNN [10]. At present, most of the frontrunners of image processing and computer vision competitions are employing deep CNN based models.The attractive feature of CNN is its ability to exploit spatial or time correlation of the data. The topology of CNN is divided into multiple learning stages composed of a combination of the convolutional layers, non-linear processing units, and subsampling layers [11]. CNNs are feedforward multilayered hierarchical networks that are similar to fully connected neural network where each layer, using a bank of convolutional kernels, performs multiple transformations [12]. Convolution operation extracts useful features from locally correlated data points. Output of the convolutional kernels is assigned to non-linear processing unit (activation function), which not only helps in learning abstractions but also emb...

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Section: Lenetmentioning

confidence: 99%

A survey of the recent architectures of deep convolutional neural networks

et al. 2020

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“…The CNN experiments were conducted using Tensorflow of Keras deep learning framework in Python. The proposed networks were trained in a GPU environment which provided more cost-effective calculations than a CPU [56][57][58][59]. We used a Volta GPU (NVIDIA Tesla V100) which has 5,376 CUDA cores and 16 GB of memory.…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

“…Remote Sens. 2019, 11, x FOR PEER REVIEW 8 of 26 effective calculations than a CPU [56][57][58][59]. We used a Volta GPU (NVIDIA Tesla V100) which has 5,376 CUDA cores and 16 GB of memory.…”

Section: Convolutional Neural Network (Cnns)mentioning

confidence: 99%

Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data

Lee

Cha

et al. 2019

Remote Sensing

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For a long time, researchers have tried to find a way to analyze tropical cyclone (TC) intensity in real-time. Since there is no standardized method for estimating TC intensity and the most widely used method is a manual algorithm using satellite-based cloud images, there is a bias that varies depending on the TC center and shape. In this study, we adopted convolutional neural networks (CNNs) which are part of a state-of-art approach that analyzes image patterns to estimate TC intensity by mimicking human cloud pattern recognition. Both two dimensional-CNN (2D-CNN) and three-dimensional-CNN (3D-CNN) were used to analyze the relationship between multi-spectral geostationary satellite images and TC intensity. Our best-optimized model produced a root mean squared error (RMSE) of 8.32 kts, resulting in better performance (~35%) than the existing model using the CNN-based approach with a single channel image. Moreover, we analyzed the characteristics of multi-spectral satellite-based TC images according to intensity using a heat map, which is one of the visualization means of CNNs. It shows that the stronger the intensity of the TC, the greater the influence of the TC center in the lower atmosphere. This is consistent with the results from the existing TC initialization method with numerical simulations based on dynamical TC models. Our study suggests the possibility that a deep learning approach can be used to interpret the behavior characteristics of TCs.

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“…For deep learning applications, the current hardware acceleration mainly depends on graphics processing unit (GPU) [13], [14], compared to traditional general-purpose processor (CPU), the core computing power of GPU is greater and easy to parallel, while the GPU's power consumption is too high for edge devices. Hundreds of watts consumption on edge devices is a huge challenge, therefore, GPU is not suitable for deployment on edge devices.…”

Section: Related Workmentioning

confidence: 99%

Design and Implementation of Deep Neural Network for Edge Computing

Zhang

Guo

et al. 2018

IEICE Trans. Inf. & Syst.

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SUMMARYIn recent years, deep learning based image recognition, speech recognition, text translation and other related applications have brought great convenience to people's lives. With the advent of the era of internet of everything, how to run a computationally intensive deep learning algorithm on a limited resources edge device is a major challenge. For an edge oriented computing vector processor, combined with a specific neural network model, a new data layout method for putting the input feature maps in DDR, rearrangement of the convolutional kernel parameters in the nuclear memory bank is proposed. Aiming at the difficulty of parallelism of two-dimensional matrix convolution, a method of parallelizing the matrix convolution calculation in the third dimension is proposed, by setting the vector register with zero as the initial value of the max pooling to fuse the rectified linear unit (ReLU) activation function and pooling operations to reduce the repeated access to intermediate data. On the basis of single core implementation, a multi-core implementation scheme of Inception structure is proposed. Finally, based on the proposed vectorization method, we realize five kinds of neural network models, namely, AlexNet, VGG16, VGG19, GoogLeNet, ResNet18, and performance statistics and analysis based on CPU, gtx1080TI and FT2000 are presented. Experimental results show that the vector processor has better computing advantages than CPU and GPU, and can calculate large-scale neural network model in real time.

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CNN Based High Performance Computing for Real Time Image Processing on GPU

Cited by 18 publications

References 5 publications

A survey of the recent architectures of deep convolutional neural networks

A survey of the recent architectures of deep convolutional neural networks

Tropical Cyclone Intensity Estimation Using Multi-Dimensional Convolutional Neural Networks from Geostationary Satellite Data

Design and Implementation of Deep Neural Network for Edge Computing

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