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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

共 10259 条记录，以下是61-70 订阅

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Designing Solutions for high-performance Communications Software Design in Network Applications

Designing Solutions for High-Performance Communications Soft...

引用

2024 International conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Agarwal, Puneet Bhuvana, J. Sharma, Bhuvnesh College of Engineering Teetthanker Mahaveer University Uttar Pradesh Moradabad India Department of Computer Science and Information Technology Bangalore India Vivekananda Global University Mechanical Engineering Jaipur India

ISBN: (纸本)9798350383348

Designing software program answers for high-performance communications in community packages is complex. It calls for cautious attention to the underlying community hardware, protocols, and algorithms. The goal is to create a communique machine with minimal latency and go-platform compatibility while maintaining robust safety and imparting excessive throughput. Designing high-overall performance conversation software should remember the wishes of the software. For example, an internet server wishes to acquire and respond to many requests concurrently;thus, the community should efficiently present more than one simultaneous connection. Then again, a video streaming application will require the network to handle heavy visitors without experiencing unexpected delays or packet loss. The demanding situations of designing a communications software device are further exacerbated using the complexity of today's networks. Exclusive protocols ever require unique optimizations and adjustments to maximize overall performance. Community topology and the environment, including the nature of the relationship kind, must also be considered. Similarly, selecting protocols is a crucial issue, as each has its benefits and barriers. To navigate these complexities efficaciously, software program developers must have widespread enjoyment and profound know-how of each protocol and community environment. They must additionally apprehend the interaction among the additives of the network;the development of excessive-overall performance conversation software layout for network applications is a challenging mission for software program engineers and builders. Despite challenges with variable community situations, stop-consumer requirements, and a wide range of gadgets, reliable and excessive overall performance software must be designed for these networks. Designing for excessive-performance communications software entails the attention and assessment of numerous factors, expertise, and lookin

关键词： algorithm communications designed designs minimal

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Temporal Fractal Pattern Recognition for Visual Working Memory Tasks Based on FMRI 21

Temporal Fractal Pattern Recognition for Visual Working Memo...

引用

21st International computer conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Jiayu, Li Ronglong, Xiong Ling, Li Center for Psychiatry and Psychology School of Life Science and Technology University of Electronic Science and Technology of China MOE Key Lab for Neuroinformation High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province Chengdu610054 China

ISBN: (纸本)9798331519254

Fractal theory is more sensitive than Pearson correlation in detecting a slight variation between cognitive states. Therefore, to uncover fractal patterns induced by visual working memory under specific category representation tasks and varying levels of load, and to establish a better classification model, this study employed the typical fractal indicators, the Hurst exponent and wavelet entropy, which characterize system (or signal) persistence and complexity. Based on this framework, we developed a multi-class machine learning model with improved classification performance. © 2024 IEEE.

关键词： Fractals

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LEAP: LLW RowHammer Mitigation System 21

LEAP: LLW RowHammer Mitigation System

引用

21st International System-on-Chip Design conference, ISOCC 2024

作者： Kim, Yoojin Ryu, Yesin Kim, Jungrae Sungkyunkwan University Electrical and Computer Engineering Suwon Korea Republic of Sungkyunkwan University Semiconductor and Display Engineering Suwon Korea Republic of

ISBN: (纸本)9798350377088

Adopting LLW DRAM in modern mobile systems has increased vulnerability to RowHammer attacks due to shorter row cycle times. This paper introduces LLW Line Encrypted Aggressor Pinning (LEAP), a high-security, high-performance, low-power in-DRAM RowHammer mitigation scheme for LLW DRAM systems. It features line-level address encryption and data pinning, aligning with LLW's characteristics, and enhances performance by 2.4% while reducing power consumption by 6.8% compared to traditional victim refresh methods. © 2024 IEEE.

关键词： Static random access storage

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LADON: high-performance Multi-BFT Consensus via Dynamic Global Ordering 25

LADON: High-Performance Multi-BFT Consensus via Dynamic Glob...

引用

20th European conference on computer Systems-EuroSys

作者： Lyu, Hanzheng Xie, Shaokang Niu, Jianyu Feng, Chen Zhang, Yinqian Beschastnikh, Ivan Univ British Columbia Okanagan Campus Vancouver BC Canada Southern Univ Sci & Technol Shenzhen Peoples R China

ISBN: (纸本)9798400711961

Multi-BFT consensus runs multiple leader-based consensus instances in parallel, circumventing the leader bottleneck of a single instance. However, it contains an Achilles' heel: the need to globally order output blocks across instances. Deriving this global ordering is challenging because it must cope with different rates at which blocks are produced by instances. Prior Multi-BFT designs assign each block a global index before creation, leading to poor performance. We propose Ladon, a high-performance Multi-BFT protocol that allows varying instance block rates. Our key idea is to order blocks across instances dynamically, which eliminates blocking on slow instances. We achieve dynamic global ordering by assigning monotonic ranks to blocks. We pipeline rank coordination with the consensus process to reduce protocol overhead and combine aggregate signatures with rank information to reduce message complexity. Ladon's dynamic ordering enables blocks to be globally ordered according to their generation, which respects inter-block causality. We implemented and evaluated Ladon by integrating it with both PBFT and HotStuff protocols. Our evaluation shows that Ladon-PBFT (resp., Ladon-HotStuff) improves the peak throughput of the prior art by approximate to 8x (resp., 2x) and reduces latency by approximate to 62% (resp., 23%), when deployed with one straggling replica (out of 128 replicas) in a WAN setting.

关键词： Multi-BFT consensus Straggler nodes Dynamic global ordering performance

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FaceCom: Towards high-fidelity 3D Facial Shape Completion via Optimization and Inpainting Guidance

FaceCom: Towards High-fidelity 3D Facial Shape Completion vi...

引用

IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Li, Yinglong Wu, Hongyu Wang, Xiaogang Qin, Qingzhao Zhao, Yijiao Wang, Yong Hao, Aimin Beihang Univ State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Peking Univ Sch & Hosp Stomatol Beijing Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing Peoples R China

ISBN: (纸本)9798350353013;9798350353006

We propose FaceCom, a method for 3D facial shape completion, which delivers high-fidelity results for incomplete facial inputs of arbitrary forms. Unlike end-to-end shape completion methods based on point clouds or voxels, our approach relies on a mesh-based generative network that is easy to optimize, enabling it to handle shape completion for irregular facial scans. We first train a shape generator on a mixed 3D facial dataset containing 2405 identities. Based on the incomplete facial input, we fit complete faces using an optimization approach under image inpainting guidance. The completion results are refined through a post-processing step. FaceCom demonstrates the ability to effectively and naturally complete facial scan data with varying missing regions and degrees of missing areas. Our method can be used in medical prosthetic fabrication and the registration of deficient scanning data. Our experimental results demonstrate that FaceCom achieves exceptional performance in fitting and shape completion tasks. The code is available at https://***/dragonylee/***.

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Ev-NeRF: Event Based Neural Radiance Field 23

Ev-NeRF: Event Based Neural Radiance Field

引用

23rd IEEE/CVF Winter conference on Applications of computer Vision (WACV)

作者： Hwang, Inwoo Kim, Junho Kim, Young Min Seoul Natl Univ Dept Elect & Comp Engn Seoul South Korea Seoul Natl Univ Interdisciplinary Program Artificial Intelligence Seoul South Korea Seoul Natl Univ INMC Seoul South Korea

ISBN: (纸本)9781665493468

We present Ev-NeRF, a Neural Radiance Field derived from event data. While event cameras can measure subtle brightness changes in high frame rates, the measurements in low lighting or extreme motion suffer from significant domain discrepancy with complex noise. As a result, the performance of event-based vision tasks does not transfer to challenging environments, where the event cameras are expected to thrive over normal cameras. We find that the multi-view consistency of NeRF provides a powerful self-supervision signal for eliminating spurious measurements and extracting the consistent underlying structure despite highly noisy input. Instead of posed images of the original NeRF, the input to Ev-NeRF is the event measurements accompanied by the movements of the sensors. Using the loss function that reflects the measurement model of the sensor, Ev-NeRF creates an integrated neural volume that summarizes the unstructured and sparse data points captured for about 2-4 seconds. The generated neural volume can also produce intensity images from novel views with reasonable depth estimates, which can serve as a high-quality input to various vision-based tasks. Our results show that Ev-NeRF achieves competitive performance for intensity image reconstruction under extreme noise and high-dynamic-range imaging.

关键词： Image sensors Solid modeling Volume measurement Lighting Cameras Loss measurement Sensors

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Noisy-Correspondence Learning for Text-to-Image Person Re-identification

Noisy-Correspondence Learning for Text-to-Image Person Re-id...

引用

IEEE/CVF conference on computer Vision and Pattern Recognition (CVPR)

作者： Qin, Yang Chen, Yingke Peng, Dezhong Peng, Xi Zhou, Joey Tianyi Hu, Peng Sichuan Univ Coll Comp Sci Chengdu 610095 Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle NSW NE1 Australia ASTAR Ctr Frontier Res CFAR Singapore Singapore ASTAR Inst High Performance Comp 1HPC Singapore Singapore SichuanNewstrong UHD Video Technol Co Ltd Chengdu 610095 Peoples R China Chengdu Ruibei Yingte Informat Technol Co Ltd Chengdu 610065 Peoples R China

ISBN: (纸本)9798350353006

Text-to-image person re-identification (TIReID) is a compelling topic in the cross-modal community, which aims to retrieve the target person based on a textual query. Although numerous TIReID methods have been proposed and achieved promising performance, they implicitly assume the training image-text pairs are correctly aligned, which is not always the case in real-world scenarios. In practice, the image-text pairs inevitably exist under-correlated or even false-correlated, a.k.a noisy correspondence (NC), due to the low quality of the images and annotation errors. To address this problem, we propose a novel Robust Dual Embedding method (RDE) that can learn robust visual-semantic associations even with NC. Specifically, RDE consists of two main components: 1) A Confident Consensus Division (CCD) module that leverages the dual-grained decisions of dual embedding modules to obtain a consensus set of clean training data, which enables the model to learn correct and reliable visual-semantic associations. 2) A Triplet Alignment Loss (TAL) relaxes the conventional Triplet Ranking loss with the hardest negative samples to a log-exponential upper bound over all negative ones, thus preventing the model collapse under NC and can also focus on hard-negative samples for promising performance. We conduct extensive experiments on three public benchmarks, namely CUHK-PEDES, ICFG-PEDES, and RSTPReID, to evaluate the performance and robustness of our RDE. Our method achieves state-of-the-art results both with and without synthetic noisy correspondences on all three datasets. Code is available at https://***/QinYang79/RDE.

关键词： Cross-modal learning Noisy Correspondence Text-to-Image Person Re-identification

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EfficientViT: Lightweight Multi-Scale Attention for high-Resolution Dense Prediction

EfficientViT: Lightweight Multi-Scale Attention for High-Res...

引用

IEEE/CVF International conference on computer Vision (ICCV)

作者： Cai, Han Li, Junyan Hu, Muyan Gan, Chuang Han, Song MIT Cambridge MA 02139 USA Zhejiang Univ Hangzhou Peoples R China Tsinghua Univ Beijing Peoples R China MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (纸本)9798350307184

high-resolution dense prediction enables many appealing real-world applications, such as computational photography, autonomous driving, etc. However, the vast computational cost makes deploying state-of-the-art high-resolution dense prediction models on hardware devices difficult. This work presents EfficientViT, a new family of high-resolution vision models with novel lightweight multi-scale attention. Unlike prior high-resolution dense prediction models that rely on heavy self-attention, hardware-inefficient large-kernel convolution, or complicated topology structure to obtain good performances, our lightweight multi-scale attention achieves a global receptive field and multi-scale learning (two critical features for high-resolution dense prediction) with only lightweight and hardware-efficient operations. As such, EfficientViT delivers remarkable performance gains over previous state-of-the-art high-resolution dense prediction models with significant speedup on diverse hardware platforms, including mobile CPU, edge GPU, and cloud GPU. Without performance loss on Cityscapes, our EfficientViT provides up to 8.8x and 3.8x GPU latency reduction over SegFormer and SegNeXt, respectively. For super-resolution, EfficientViT provides up to 6.4x speedup over Restormer while providing 0.11dB gain in PSNR.

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Uncertainty-Guided Joint Semi-supervised Segmentation and Registration of Cardiac Images 31st

Uncertainty-Guided Joint Semi-supervised Segmentation and Re...

引用

31st International conference on Multimedia Modeling

作者： Chen, Junjian Yang, Xuan Shenzhen Univ Coll Comp Sci & Software Engn Guangdong Prov Key Lab Popular High Performance C Shenzhen Peoples R China

ISBN: (纸本)9789819620708;9789819620715

Aleatoric uncertainty negatively impacts registration and segmentation results for medical image analysis. In this paper, we propose an uncertainty-guided framework for the joint semi-supervised segmentation and registration of cardiac images, aiming to take advantage of both tasks for each other. We propose a semi-supervised segmentation framework by predicting statistical shape models of the heart and generating uncertainty maps to guide anchor selection in pixel-level contrastive learning. Besides, we develop a registration network to predict the deformation vector field (DVF) and registration uncertainty, where the registration uncertainty ensures the registration model focuses on regions with high confidence. By employing estimated DVFs, additional constraints between segmentation results are embedded as losses to further improve segmentation and registration accuracy at the same time. The experimental results show that our proposed framework outperforms the state-of-the-art registration and segmentation networks.

关键词： Image segmentation Image registration Contrastive learning Statistical shape model Aleatoric uncertainty

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Optimizing Direct Convolutions on ARM Multi-Cores 23

Optimizing Direct Convolutions on ARM Multi-Cores

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2023 International conference for high performance Computing, Networking, Storage and Analysis, SC 2023

作者： Wang, Pengyu Yang, Weiling Fang, Jianbin Dong, Dezun Huang, Chun Zhang, Peng Tang, Tao Wang, Zheng College of Computer Science and Technology National University of Defense Technology Changsha China School of Computing University of Leeds Leeds United Kingdom

ISBN: (纸本)9798400701092

Convolution kernels are widely seen in deep learning workloads and are often responsible for performance bottlenecks. Recent research has demonstrated that a direct convolution approach can outperform the traditional convolution implementation based on tensor-to-matrix conversions. However, existing approaches for direct convolution still have room for performance improvement. We present nDirect, a new direct convolution approach that targets ARM-based multi-core CPUs commonly found in smartphones and HPC systems. nDirect is designed to be compatible with the data layout formats used by mainstream deep learning frameworks but offers new optimizations for the computational kernel, data packing, and parallelization. We evaluate nDirect by applying it to representative convolution kernels and demonstrating its performance on four distinct ARM multi-core CPU platforms. We compare nDirect against state-of-the-art convolution optimization techniques. Experimental results show that nDirect gives the best overall performance across evaluation scenarios and platforms. © 2023 ACM.

关键词： Convolution

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