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Programming and Mapping for Mixed Heterogeneous Devices: The Case of Optical Flow

Programming and Mapping for Mixed Heterogeneous Devices: The...

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Euromicro conference on parallel, distributed and Network-Based processing

作者： Sergio Alonso Pascual Arturo Gonzalez-Escribano Departamento de Informática Universidad de Valladolid. ETSII Valladolid Spain

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Current parallel systems are increasingly heterogeneous, mixing devices of different types and computing capabilities. Exploiting multiple different devices for the same application continues to be a challenge that ranges from technical problems related to synchronizing and communicating diverse devices to problems of load distribution and flexibility to adjust the computation to the platform resources. In this work, we study the problem of using and extending a heterogeneous portability layer to program and adapt HSOpticalFlow to heterogeneous platforms. HSOpticalFlow is a streaming application to estimate the apparent movement of objects in a sequence of images. It is a simple but characteristic example of the structure of applications based on multilevel ILS (Iterative Loop Stencil), also known as multi-grid methods, applied to a sequence of inputs. Starting from the original CUDA reference code, we present a methodology and programming techniques based on the Controller programming model to implement it as a pipeline among multiple devices. We discuss a technique to determine a proper work partition and mapping for a set of devices. This allows for building very efficient parallel solutions, using similar devices or taking advantage of devices with lower computing power, to reduce the load and increase the productivity of more powerful ones. We present the results of an experimental study using several GPUs of different vendors, architectures, and generations, showing that this solution allows combinations of devices to be efficiently exploited to improve performance. Specifically, the results include speedups of 1.91x using two NVIDIA A100 GPUs and 1.21x using one NVIDIA V100 GPU and one AMD WX9100 GPU, which is about $3 x$ slower than the NVIDIA GPU for this application.

关键词： Productivity Performance evaluation Computational modeling Pipelines Graphics processing units Computer architecture Programming Streaming media Optical flow Tuning

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Prediction and Evaluation Method of Modification Effect of Large-Scale DBD Insulation Materials Based on distributed Current Measurement and Neural Network Model 1st

Prediction and Evaluation Method of Modification Effect of L...

引用

1st Electrical Artificial Intelligence conference, EAIC 2024

作者： Yizhuo, Wang Zhonlian, Li Long, Li Runhua, Li Xinglei, Cui Zhi, Fang School of Electrical Engineering and Control Science Nanjing Tech University Nanjing211816 China

ISBN: (纸本)9789819648559

Wide dielectric barrier discharge (DBD) has broad application prospects in the modification of insulating materials, but the aging of the electrode directly affects the modification effect in the application process. As the size of the DBD device increases, the real-time evaluation of its modification effect becomes more complicated. Therefore, this paper proposes a real-time prediction and evaluation method for the modification effect of wide DBD insulation materials based on distributed current measurement and neural network model. The operating condition parameters such as DBD excitation voltage amplitude, repetition frequency, discharge working gas flow rate and reaction medium flow rate were changed. The discharge current at different positions was measured by self-made current coil. The water contact angle and flashover voltage at the corresponding position were tested experimentally as the evaluation criteria of the modification effect. The feature extraction of distributed current is carried out by manual and image recognition methods, and the prediction and evaluation models are established by BP neural network and convolutional neural network (CNN) respectively. The accuracy and generalization ability of the two models are compared. The results show that the CNN model based on image recognition has higher accuracy and generalisation ability in predicting the water contact angle and flashover voltage on the material surface compared to the BP neural network model based on manual feature extraction. Compared with the BP neural network, the CNN model reduces the mean absolute error (MAE) by 41.3% and the root mean square error (RMSE) by 36.1% in predicting the water contact angle of the material surface, and the mean absolute error (MAE) reduces by 47.7% and the RMSE reduces by 40.2% in the flashover voltage. The experimental results with different processing distances are used to examine the generalisation ability of the two models, and the results show that

关键词： Electric insulating materials

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Boosting Text-To-image Generation via Multilingual Prompting in Large Multimodal Models

Boosting Text-To-Image Generation via Multilingual Prompting...

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

作者： Yongyu Mu Hengyu Li Junxin Wang Xiaoxuan Zhou Chenglong Wang Yingfeng Luo Qiaozhi He Tong Xiao Guocheng Chen Jingbo Zhu School of Computer Science and Engineering Northeastern University Shenyang China School of Economics and Management Dalian Jiaotong University Dalian China NiuTrans Research Shenyang China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Previous work on augmenting large multimodal models (LMMs) for text-to-image (T2I) generation has focused on enriching the input space of in-context learning (ICL). This includes providing a few demonstrations and optimizing image descriptions to be more detailed and logical. However, as demand for more complex and flexible image descriptions grows, enhancing comprehension of input text within the ICL paradigm remains a critical yet underexplored area. In this work, we extend this line of research by constructing parallel multilingual prompts aimed at harnessing the multilingual capabilities of LMMs. More specifically, we translate the input text into several languages and provide the models with both the original text and the translations. Experiments on two LMMs across 3 benchmarks show that our method, PMT2I, achieves superior performance in general, compositional, and fine-grained assessments, especially in human preference alignment Additionally, with its advantage of generating more diverse images, PMT2I significantly outperforms baseline prompts when incorporated with reranking methods. Our code and parallel multilingual data can be found at https://***/takagi97/PMT2I.

关键词： Translation Codes image synthesis Text to image Signal processing Benchmark testing Boosting Acoustics Multilingual Speech processing

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GCAT: Gated Convolutional Attention Transformer for Efficient image Super-Resolution

GCAT: Gated Convolutional Attention Transformer for Efficien...

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

作者： Zhijian Wu Kaiyi Feng Dingjiang Huang School of Data Science and Engineering East China Normal University Shanghai China School of Control and Computer Engineering North China Electric Power University Hebei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Recently, Transformer-based methods have achieved impressive performance in many computer vision tasks (e.g., image super-resolution (SR)) due to the advantages of long-range modeling. However, the computational cost requirement renders these methods unsuitable on resource-constrain devices, especially for image SR tasks involving high-resolution images. In this paper, we propose a concise and effective Gated Convolutional Attention Unit (GCAU) that uses cheap convolutional operations. Specifically, GCAU consists of Convolutional Transposed Attention (CTA) and Locally-enhanced Gating (LeG) in parallel. The former allows for efficient modeling of the global relational interactions by calculating cross-covariance across channels dimension, while the latter controls the information flow from the former directing the network to focus on more refined image attributes. Without bells and whistles, we present a simple SR Transformer GCAT by cascading the GCAUs. Extensive experimental results demonstrate that our GCAT achieves state-of-the-art performance among the existing efficient SR methods with significantly less complexity. Especially, GCAT is on average 5× faster than SwinIR-light with comparable performance.

关键词： Legged locomotion Performance evaluation Computer vision Convolution Computational modeling Superresolution Logic gates Transformers Speech processing Signal resolution

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Hazy Remote Sensing image Semantic Segmentation with Weak Annotations via Pre-training Optimization and Co-training

Hazy Remote Sensing Image Semantic Segmentation with Weak An...

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

作者： Junda Xu Libao Zhang School of Artificial Intelligence Beijing Normal University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In recent years, weakly supervised semantic segmentation has emerged as a prominent research topic in the field of remote sensing image semantic segmentation due to its cost-effective labeling advantages. However, the presence of haze in remote sensing images poses significant challenges to accurate semantic segmentation. Despite the numerous haze removal methods developed for remote sensing images, their efficacy in the subsequent task of semantic segmentation remains inadequate. To address these issues, this paper aims to enhance the robustness of the segmentation network against haze interference by proposing a weakly supervised semantic segmentation framework based on pre-training optimization and dual-network co-training. The proposed approach employs a pseudo-label optimization network to effectively filter out noise interference and subsequently trains two parallel segmentation networks that mutually guide each other for enhanced robustness. Additionally, an edge optimization loss is introduced to improve prediction accuracy by incorporating both edge and texture information. Experimental comparisons with alternative methods across multiple datasets validate the superior performance of our proposed method.

关键词： Accuracy Semantic segmentation image edge detection Noise Interference Robustness Sensors Speech processing Optimization Remote sensing

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Dynamic Bio-Information image Recognition of Cancel Cell Death based on parallel Multiple Artificial Intelligence Modules

Dynamic Bio-Information Image Recognition of Cancel Cell Dea...

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International conference on Advanced Communication Technology (ICACT)

作者： Ming-Shen Jian Yi-Ling Ye Wen-Yu Chung Wen-Xuan Li Yan-Ting Lin Bo-Yuan Huang Javeria Sikandar Yu-Jung Chang Dept. of CSIE CCIS Lab National Formosa University Yunlin Taiwan Dept. of Biotechnology National Formosa University Yunlin Taiwan

ISBN: (数字)9791188428137

ISBN: (纸本)9798331507602

This investigation purpose a unique Dynamic Bio-Information image Recognition System (DBIRS) built for the real-time monitoring of cancer cell apoptosis produced by available medical therapy. Utilizing advances in microscopy, AI-based image recognition, and parallel computing across distributed edge devices, the proposed system dramatically boosts the precision and efficiency of cellular investigation. By deploying numerous AI modules adapted to distinct cellular states, the system provides automatic, high-accuracy recognition of morphological changes in cancer cells, hence minimizing the need on manual procedures. This technique offers remote monitoring and fast data processing via cloud integration, permitting continuous observation and timely intervention. The system's efficiency is proven through trials on K-562 cells, where it attained a recognition accuracy of 97.41%. These outcomes underline the potential of the proposed method to refine cytotoxicity assays, delivering a strong tool for increasing the efficacy and safety of immune cell treatments in clinical and research settings.

关键词： Training image recognition Accuracy Real-time systems Safety Artificial intelligence Biological cells Remote monitoring Biomedical imaging Cancer

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PB-UAP: Hybride Universal Adversarial Attack for image Segmentation

PB-UAP: Hybride Universal Adversarial Attack for Image Segme...

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

作者： Yufei Song Ziqi Zhou Minghui Li Xianlong Wang Hangtao Zhang Menghao Deng Wei Wan Shengshan Hu Leo Yu Zhang Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

With the rapid advancement of deep learning, the model robustness has become a significant research hotspot, i.e., adversarial attacks on deep neural networks. Existing works primarily focus on image classification tasks, aiming to alter the model’s predicted labels. Due to the output complexity and deeper network architectures, research on adversarial examples for segmentation models is still limited, particularly for universal adversarial perturbations. In this paper, we propose a novel universal adversarial attack method designed for segmentation models, which includes dual feature separation and low-frequency scattering modules. The two modules guide the training of adversarial examples in the pixel and frequency space, respectively. Experiments demonstrate that our method achieves high attack success rates surpassing the state-of-the-art methods, and exhibits strong transferability across different models.

关键词： Training image segmentation Perturbation methods Semantics Scattering Signal processing Predictive models Network architecture Robustness Speech processing

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parallel training and testing methods for complex image processing algorithms on distributed, heterogeneous, unreliable, and non-dedicated resources

Parallel training and testing methods for complex image proc...

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conference on parallel processing for Imaging Applications

作者： Usamentiaga, Ruben Garcia, Daniel F. Molleda, Julio Sainz, Ignacio Bulnes, Francisco G. Univ Oviedo Dept Comp Sci Gijon 33204 Asturias Spain

ISBN: (纸本)9780819484093

Advances in the image processing field have brought new methods which are able to perform complex tasks robustly. However, in order to meet constraints on functionality and reliability, imaging application developers often design complex algorithms with many parameters which must be finely tuned for each particular environment. The best approach for tuning these algorithms is to use an automatic training method, but the computational cost of this kind of training method is prohibitive, making it inviable even in powerful machines. The same problem arises when designing testing procedures. This work presents methods to train and test complex image processing algorithms in parallel execution environments. The approach proposed in this work is to use existing resources in offices or laboratories, rather than expensive clusters. These resources are typically non-dedicated, heterogeneous and unreliable. The proposed methods have been designed to deal with all these issues. Two methods are proposed: intelligent training based on genetic algorithms and PVM, and a full factorial design based on grid computing which can be used for training or testing. These methods are capable of harnessing the available computational power resources, giving more work to more powerful machines, while taking its unreliable nature into account. Both methods have been tested using real applications.

关键词： parallelization on unreliable non-dedicated resources parallel training parallel genetic algorithms image processing based on grid computing

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Gossamer: Scaling image processing and Reconstruction to Whole Brains 17th

Gossamer: Scaling Image Processing and Reconstruction to Who...

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17th International conference on Brain Informatics, BI 2024

作者： Marrett, Karl Moradi, Keivan Park, Chris Sin Yan, Ming Choi, Chris Zhu, Muye Akram, Masood Nanda, Sumit Xue, Qing Mun, Hyun-Seung Gutierrez, Adriana E. Rudd, Mitchell Zingg, Brian Magat, Gabrielle Wijaya, Kathleen Dong, Hongwei Yang, X. William Cong, Jason Department of Computer Science University of California Los Angeles Los Angeles United States Center for Neurobehavioral Genetics Semel Institute for Neuroscience and Human Behavior University of California Los Angeles Los Angeles United States Department of Psychiatry and Biobehavioral Sciences David Geffen School of Medicine University of California Los Angeles Los Angeles United States Department of Neuroscience David Geffen School of Medicine University of California Los Angeles Los Angeles United States

ISBN: (纸本)9789819632930

Neuronal reconstruction–a process that transforms image volumes into 3D geometries and skeletons of cells–bottlenecks the study of brain function, connectomics and pathology. Domain scientists need exact and complete segmentations to study subtle topological differences. Existing methods are disk-bound, dense-access, coupled, single-threaded, algorithmically unscalable and require manual cropping of small windows and proofreading of skeletons due to low topological accuracy. Designing a data-intensive parallel solution suited to a neurons’ shape, topology and far-ranging connectivity is particularly challenging due to I/O and load-balance, yet by abstracting these vision tasks into strategically ordered specializations of search, we progressively lower memory by 4 orders of magnitude. This enables 1 mouse brain to be fully processed in-memory on a single server, at 67× the scale with 870× less memory while having 78% higher automated yield than APP2, the previous state of the art in performant reconstruction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Neurons

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Single-View Reconstruction via Decoupled 3D Gaussian Splatting

Single-View Reconstruction via Decoupled 3D Gaussian Splatti...

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

作者： Sheng Liu Shiming Zhu Huilong Pi Yunchuan Qin Zhuo Tang Ruihui Li College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Creating high-quality 3D object representations from a single-view image is challenging. Existing methods tend to infer the geometry and texture information simultaneously within a shared network. However, decoding geometry and texture from a unified network often leads to their entanglement, causing geometric structure collapse or floating artifacts. After revisiting this task, we propose a single-view reconstruction framework based on 3D Gaussian Splatting. The key idea is to decouple Gaussian position attribute generation from texture feature generation. Technically, our framework combines a Geometry Generator, a Texture Generator, and a Gaussian Attributes Decoder. Two parallel branches, Geometry Generator and Texture Generator, aim for point cloud prediction and texture optimization, respectively. Then the Gaussian Attributes Decoder integrates the generated position and texture attributes into a coherent Gaussian point cloud, facilitating efficient novel view synthesis. Extensive qualitative and quantitative evaluations of public datasets demonstrate that our method consistently outperforms existing methods in terms of reconstruction quality and inferring efficiency.

关键词： Geometry Point cloud compression Three-dimensional displays Signal processing Reconstruction algorithms Generators Decoding Speech processing image reconstruction Optimization

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