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检索条件"任意字段=Conference on Parallel and Distributed Methods for Image Processing"

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Design and Implementation of Optimized Resource Low-Power SoC Multi-Soft-Processor FPGA Hardware Acceleration Architecture for Fast Digital image processing

Design and Implementation of Optimized Resource Low-Power So...

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IEEE National conference on Aerospace and Electronics (NAECON)

作者： L. Namou D.B Megherbi Department of Electrical and Computer Engineering CMINDS Research Center University of Massachusetts Lowell MA

This paper focuses on designing, implementing, and optimizing a distributed multi-soft-processor SoC low-power FPGA-based software-hardware co-design architecture for fast digital image processing computing acceleration. The paper demonstrates an on and off-the-chip augmented hardware architecture implementation to overcome Memory, Cost, Size, and Power prohibitive resource constraints. This implementation experimentally achieves an optimized accelerated digital image processing and digital filtering execution time. The method achieves 80 times computational speedup with the proposed augmented hardware-accelerated optimization scheme compared to an initial practical implementation without hardware acceleration. This design is implemented on an SoC Altera Cyclone- V FPGA. In this design, an FPGA Multi Softcore Processor is instantiated in the fabric and interfaced with an external SDRAM via a 16-bit Memory Controller Intellectual Property (IP) core. An 8-bit intensity resolution image (up to 1280 x 1280 pixels, but not limited) was imported from a PC (via Serial Communication) to the FPGA for Sobel spatial filter processing. Each Soft Core was memory mapped to each image segment to create a parallel multiprocessing architecture. Multiple Sobel Filters are written and synthesized in the fabric. The Register Transfer Logic (RTL) code is written in VHDL and Verilog as a Hardware Description Language (HDL) to accelerate the design implementation. A different number of Softcore Processors in conjuncture with accelerated hardware are tested to compare the resulting computational efficiency. The timings between accessing the on-chip SRAM and the off-chip SDRAM can also be compared. In the final analysis, understanding that the FPGA Softcore using C is quick to implement and flexible to interface with, but it is a non-deterministic and sequentially executed process, and that the FPGA Fabric using HDL is rigid and constraint-driven, but no overhead, implemented in fabric, det

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Application of distributed computation of texture features to the analysis of biomedical images

Application of distributed computation of texture features t...

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18th International Scientific and Technical conference on Optical Technologies for Communications

作者： Ilyasova, Nataly Yu Demin, Nikita S. Shirokanev, Alexandr S. Samara Natl Res Univ Moskovskoye Shosse 34 Samara 443086 Russia RAS IPSI RAS Branch FSRC Crystallog & Photon Molodogvardeyskaya 151 Samara 443001 Russia

ISBN: (数字)9781510644250

ISBN: (纸本)9781510644250

Medical applications are among the tasks of optical technology. The processing of two-dimensional optical signals and images is an urgent task today. One of the most dangerous eye diseases is diabetic macular retinopathy. The first stage in the laser coagulation operation is the stage of fundus image segmentation. The calculation of texture features for solving this problem takes a lot of time. In this paper, we consider the use of a high-performance algorithm for calculating texture features based on distributed computing to speed up the processing and analysis of medical images. Various use cases of the high-performance algorithm on a single node were investigated and compared with sequential and parallel algorithms. The high-performance algorithm achieves a 40x speedup and more under some parameters. Using a high-performance algorithm, analysis and segmentation is performed in less than 1 minute for standard images. The use of a high-performance algorithm for the analysis and segmentation of fundus images avoids the need for a sequential skip-step algorithm, which, due to interpolation, reduces the execution time, but at the same time, accuracy is lost.

关键词： fundus images image processing laser coagulation image segmentation eye retina texture features high-performance algorithm

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Some Intriguing Observations on the Learnt Matrices in Deep Unfolded Networks

Some Intriguing Observations on the Learnt Matrices in Deep ...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Kartheek Kumar Reddy Nareddy Inbasekaran Perumal Chandra Sekhar Seelamantula Department of Electrical Engineering Indian Institute of Science Bengaluru Institute of Data Science German Aerospace Center Jena Germany Department of Electronics and Communication Engineering National Institute of Technology Mangaluru Karnataka

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep-unfolded networks (DUNs) have set new performance benchmarks in fields such as compressed sensing, image restoration, and wireless communications. DUNs are built from conventional iterative algorithms, where an iteration is transformed into a layer/block of a network with learnable parameters. Despite their huge success, the reasons behind their superior performance over their iterative counterparts are not fully understood. This paper focuses on enhancing the explainability of DUNs by investigating potential reasons behind their superior performance over traditional iterative methods. We concentrate on the Learnt Iterative Shrinkage-Thresholding Algorithm (LISTA), a foundational contribution that achieves sparse recovery with significantly fewer layers than its iterative counterpart, ISTA. Our findings reveal that the learnt matrices in LISTA always have Gaussian distributed entries regardless of whether the sensing matrix is random Gaussian, Bernoulli, exponential, or uniform. The findings also show that the singular values of the learnt matrices exceed unity, despite which, the reconstruction scheme is stable. We conjecture that the activation function may have a role to play in ensuring stability. We also present an unbiasing technique that substantially improves the sparse recovery performance by reestimating the amplitudes based on the converged support.

关键词： Training Wireless communication Accuracy Superresolution Signal processing algorithms Gaussian distribution Iterative algorithms Sparse matrices image reconstruction Convergence

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Review of parallel processing methods for Big image Data Applications

Review of Parallel Processing Methods for Big Image Data App...

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International conference on Communication, Computing and Electronics Systems, ICCCES 2019

作者： Vigneshwari, K. Kalaiselvi, K. Department of Computer Science VELS Institute of Science Technology and Advanced Studies Chennai600117 India Formerly Vels University Chennai600117 India

ISBN: (纸本)9789811526114

The coexistence of technologies, like big data application, cloud computing, and the numerous images in the Web has paved the need for new image processing algorithms that exploit the processed image for diverse applications. There arises a need for new image processing algorithms to utilize the processed image for diverse applications though many techniques with variations exist. Ultimately, the enduring issue is to enhance the effectiveness of huge image processing and to maintain the combination of the same with recent works. This paper presents a review of the newest progress in researches on parallel processing methods for the processing of big data. Initially, the reviews about the parallel processing methods were carried out by highlighting some promising parallel processing methods in recent studies, such as the representation of MapReduce (MR) framework, distributed, parallel methods, and Hadoop framework. Subsequently, focus on analysis and deliberations about the challenges and promising solutions of parallel computing methods on big data in various applications and on image processing applications were made and concluded with a summary of number of open problems and research areas. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Big data

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parallel image Encryption Algorithm Using Partitioned Cellular Automata on Graphic Processor Unit

Parallel Image Encryption Algorithm Using Partitioned Cellul...

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Omni-layer Intelligent Systems (COINS), International conference on

作者： Mahmood Fazlali Amirhossein Alihoseini Ebrahim Zarei Myasar Tabani Seyedali Pourmoafi Cybersecurity and Computing Systems Research Group University of Hertfordshire Hatfiled UK Dept. of data and Computer Sciensce Faculty of Mathematical sciences Shahid Beheshti University Tehran Iran Dep. of Computer Science Khansar Campus University Of Isfahan Isfahan Iran

image encryption is a reliable method for securely transmitting images over a network. The time required to encrypt and decrypt an image in online applications is also very important. Although cellular automata cryptography is an appropriate technique for parallelizing and accelerating cryptographic methods, its capacity cannot be demonstrated only in multi-core platforms. Thus, it is needed to parallelize cellular automata cryptography on Graphic Processor Units (GPUs) in order to significantly decrease the encryption/decryption time. In this paper, we propose a new parallel algorithm for two-dimensional cellular automata cryptography that is implemented on GPU. The proposed algorithm uses multiple threads at once to accelerate the bit-level permutation and substitution operations by taking into account the capacity of cellular automata in parallel processing. According to the study experimental findings, the proposed algorithm performs faster on GPU compared to a multicore platform while maintaining the same level of security in comparison to the serial algorithm.

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G-Litter Marine Litter Dataset Augmentation with Diffusion Models and Large Language Models on GPU Acceleration 33

G-Litter Marine Litter Dataset Augmentation with Diffusion M...

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33rd Euromicro International conference on parallel, distributed, and Network-Based processing, PDP 2025

作者： Mellone, Gennaro De Vita, Ciro Giuseppe Nardo, Emanuel Di Coviello, Giuseppe Luccio, Diana Di Aucelli, Pietro Patrizio Ciro Ciaramella, Angelo Montella, Raffaele University of Napoli Parthenope Department of Science and Technology Napoli Italy University of Naples Parthenope Dep. of Science and Technologies Naples Italy Integrated System NEC Laboratories America Inc PrincetonNJ United States

ISBN: (纸本)9798331524937

Marine litter detection is crucial for environmental monitoring, yet the imbalance in existing datasets limits model performance in identifying various types of waste accurately. This paper presents an efficient data augmentation pipeline that combines generative diffusion models (e.g., Stable Diffusion) and Large Language Models (LLMs) to expand the G-Litter dataset, a marine litter dataset designed for autonomous detection in heterogeneous environments. Leveraging scalable diffusion models for image generation and Alpaca LLMs for diverse prompt generation, our approach augments underrepresented classes by generating over 200 additional images per class, significantly improving the dataset's balance. Training G-Litter augmented dataset using YOLOv8 for object detection demonstrated an increase in detection performance, improving recall by 7.82% and mAP50 by 3.87% (compared with baseline results). This study emphasizes the potential for combining generative AI with HPC resources to automate data augmentation on large-scale, unstructured datasets, particularly in edge computing contexts for real-time marine monitoring. The models were tested on real videos captured during simulated missions, demonstrating a superior ability to detect submerged objects in dynamic scenarios. These results highlight the potential of generative AI techniques to improve dataset quality and detection model performance, laying the foundation for further expansion in real-time marine monitoring. © 2025 IEEE.

关键词： image enhancement

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Cloud-Driven AI: Enhancing Machine Learning Performance Through distributed Computing

Cloud-Driven AI: Enhancing Machine Learning Performance Thro...

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International conference on ICT in Business Industry & Government (ICTBIG)

作者： Kunal Dixit Himani Parul Bhardwaj Ritu Sachdeva School of Computer Applications Manav Rachna International Institute of Research & Studies Faridabad India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

The exponential data volume and complexity of Machine Learning (ML) algorithms has resulted in an increase in computational limitations, which affects artificial intelligence [AI]. This research seeks to establish whether cloud-based distributed computing can effectively address these limitations and improve the performance of ML. Our approach is a new architecture that deploys the computing load over a cloud framework in order to distribute and process in parallel some of the computational majorities of learning algorithms. To further enhance the application’s efficiency of processing the circuit data, a combination of adaptive resource allocation techniques, data partitioning strategies, and a new synchronization protocol has been proposed and implemented. This framework was assessed with deep neural networks, random forests, gradient-boosting machines to different datasets, and complex tasks. Focusing on the obtained results, it becomes possible to state that the proposed methods yield up to 87% improvement in the training speed and 5–12% increase in the accuracy of the models compared to the traditional implementations in single nodes. Moreover, our approach proved its capability of scalability did not degrade the performance and achieved near-linear speedup throughout the distributed nodes of up to 1000. These results propose that cloud-based AI can significantly reduce time to on-ramp for training numerous models on larger datasets. In light of this, distributed computing in the cloud offers a way to meet the computational demands of today’s AI applications while opening the door to new advancements in ML throughout different domains.

关键词： Training Machine learning algorithms Protocols Scalability Government Partitioning algorithms Synchronization Resource management distributed computing Random forests

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Single Node Acceleration of Generative Adversarial Networks using HPC for image Analytics 22

Single Node Acceleration of Generative Adversarial Networks ...

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Proceedings of the 2022 5th International conference on Computational Intelligence and Intelligent Systems

作者： Aswathy Ravikumar Harini Sriraman School of Computer Science and Engineering Vellore Institute of Technology India

ISBN: (纸本)9781450397612

Generative Adversarial Networks (GAN) are approaches that are utilized for data augmentation, which facilitates the development of more accurate detection models for unusual or unbalanced datasets. Computer-assisted diagnostic methods may be made more reliable by using synthetic pictures generated by GAN. Generative adversarial networks are challenging to train because too unpredictable training dynamics may occur throughout the learning process, such as model collapse and vanishing gradients. For accurate and faster results the GAN network need to trained in parallel and distributed manner. We enhance the speed and precision of the Deep Convolutional Generative Adversarial Networks (DCGAN) architecture by using its parallelism and executing it on High-Performance Computing platforms. The effective analysis of a DCGAN in Graphic processing Unit and Tensor processing Unit platforms in which each layer execution pattern is analyzed. The bottleneck is identified for the GAN structure for each execution platforms. The Central processing Unit is capable of processing neural network models, but it requires a great deal of time to do it. Graphic processing Unit in contrast, side, are a hundred times quicker than CPUs for Neural Networks, however, they are prohibitively expensive compared to CPUs. Using the systolic array structure, TPU performs well on neural networks with high batch sizes but in GAN the shift between CPU and TPU is huge so it does not perform well.

关键词： Generative Adversarial Network High-Performance Computing Graphic processing Unit Tensor processing Unit

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ROGC: Role-Oriented Graph Convolution Based Multi-Agent Reinforcement Learning

ROGC: Role-Oriented Graph Convolution Based Multi-Agent Rein...

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Liu, Yuntao Li, Yuan Xu, Xinhai Liu, Donghong Dou, Yong National University of Defense Technology National Laboratory for Parallel and Distributed Processing China Academy of Military Sciences China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

The role-oriented learning approach could improve the performance of multi-agent reinforcement learning by decomposing complex multi-agent tasks into different roles. However, due to the dynamic environment and interactions among agents, the role undertaken by an agent changes rapidly with time going on. Therefore, the roles of agents should be adapted to the varying situation during the learning process. In this paper, we propose a role-oriented graph convolution based multi-agent reinforcement learning framework (ROGC). Firstly, we design a role assigner based on samples generated from the environment to learn roles for classifying agents into different groups. To further enhance cooperation among agents in the same group for higher performance, we design a graph convolutional module to achieve intra-role communications based on discovered roles. With roles and extracted role features, we design a role-oriented policy learning module that embeds the role information into the algorithm and generates effective policies for individuals. Further, we introduce an auto-encoder to learn the intra-role cooperation knowledge in the graph convolutional module, which ensures our framework executes in a decentralized way. Extensive experiments show that our framework can learn dynamic roles and make full use of learned roles, which makes it outperform popular MARL methods. © 2022 IEEE.

关键词： Reinforcement learning

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End-to-End Medical image Denoising via Cycle-consistent Generative Adversarial Network

End-to-End Medical Image Denoising via Cycle-consistent Gene...

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2021 International conference on Information Science, parallel and distributed Systems, ISPDS 2021

作者： Yan, Chenggeng Chen, Hu Yang, Zhao Sichuan University College of Computer Science Chengdu China Sichuan University National Key Laboratory of Fundamental Science on Synthetic Vision Chengdu China

ISBN: (纸本)9781665417389

Since the radiation of X-ray is detrimental to patients, low-dose computed tomography (CT) has been developed in medical field. Nevertheless, it may degrade the quality of CT images and affect the clinical diagnosis. To address this issue, various advanced low-dose CT denoising methods have been developed to improve the quality of CT images. The majority of these methods require well-matched low-dose CT and normal-dose CT images for the training. These methods have made much progress in low-dose CT denoising field. However, it is not practicable in medical field to acquire well-paired images in medical field which restricts the application of these methods. To deal with this matter, we propose an improved end-to-end CT image denoising without aligned low-dose and normal-dose CT images inspired by Cycle-consistent Generative Adversarial Network. This method can learn the data distributions of low-dose CT and normal-dose CT in order to build a mapping from low-dose CT images to normal-dose CT images without paired CT images. Additionally, the perceptual loss is integrated into the proposed method to enhance the visual quality of denoised images. The experiments on the real clinic CT dataset show our methods can not only suppress noise but also preserve structural information. © 2021 IEEE.

关键词： Generative adversarial networks

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