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A Noc Centric Low Overhead Multi-chip Interconnection Technology 23

A Noc Centric Low Overhead Multi-chip Interconnection Techno...

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23rd IEEE International conference on high performance Computing and Communications, 7th IEEE International conference on Data Science and Systems, 19th IEEE International conference on Smart City and 7th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Hu, Xing Zhang, Yang Mao, Li Shen, Jie Xing, Zuocheng College of Computer National University of Defense Technolgy ChangSha China College of Microelectronics Xi'an University of Electronic Science and Technology Xi'an China

ISBN: (纸本)9781665494571

Improving the performance of processors is one of the main tasks of high-performance processor design. It is an effective way to improve the processor's performance through high-speed inter-chip interconnection, which utilizes multiple chips to build one unified computing system with higher performance. The existing multi-chip interconnection technologies, such as CCIX, CXL and NVLink, are faced with the problems of not open protocol, high hardware overhead and difficulty to reuse with existing high-speed peripherals. Considering the limitation of current multi-chip interconnection, we adopt the network centered idea to design FT_Link open architecture, which realizes the interconnection of multiple chips in a low-cost way and enables requests to access other chip spaces transparently. FT_Link system is built on common logic layer and physical layer of the existing high-speed peripherals, making it flexible and versatile. Tests and synthesis results show that FT-Link technology has the feature of high throughput and low latency with very small hardware overhead. Its performance bottleneck depends on the FIFO depth of the high-speed peripheral logical layer which it adopts. © 2021 IEEE.

关键词： program processors Protocols Smart cities Throughput Physical layer Hardware Hazards

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Real-Time Driver Fatigue Detection Method Based on Comprehensive Facial Features 23rd

Real-Time Driver Fatigue Detection Method Based on Comprehen...

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23rd International conference on Algorithms and Architectures for Parallel Processing (ICA3PP)

作者： Zheng, Yihua Chen, Shuhong Wu, Jianming Chen, Kairen Wang, Tian Peng, Tao Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou Guangdong Peoples R China Beijing Normal Univ BNU UIC Inst Artificial Intelligence & Future Net Zhuhai Guangdong Peoples R China

ISBN: (纸本)9789819708000;9789819708017

In recent years, there have been frequent cases of vehicle accidents caused by fatigued driving, leading to considerable economic losses and a high number of casualties. Accordingly, it has an important social significance for avoiding accident risks to remind tried drivers to take a break promptly. Fatigue driving detection based on facial feature recognition technology has attracted much attention due to its non-invasive, low-cost, and convenient detection advantages. However, the current fatigue driving technology faces the challenge of balancing real-time performance and accuracy in practical applications. Therefore, a fatigue driving detection model based on deep learning is proposed to address this issue. This model includes modules for object detection, head pose estimation, fatigue detection, and distraction detection. First, this paper proposes a facial feature detection algorithm based on YOLOv5 and FSA-Net, which can quickly detects the driver's eye state, mouth state, and head state. Second, in order to better avoid accident risks, the designed system detects driver distraction behaviors during driving process from both cognitive distraction and visual distraction perspectives based on the facial feature detection algorithm. Finally, a comprehensive dangerous driving behavior detection and warning system based on closed-eye detection, yawning detection, 3D head pose estimation, and object detection is designed by integrating multiple fatigue and distraction indicators. The experimental results show that the developed detection and warning system has high detection accuracy, which can provide timely warning when dangerous driving behavior occurs and helps ensure driving safety.

关键词： object detection computer vision fatigue driving

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Computational Efficiency of Deep Learning-Based Super Resolution Methods for 5G-NR Channel Estimation

Computational Efficiency of Deep Learning-Based Super Resolu...

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2024 IEEE Latin-American conference on Communications, LATINCOM 2024

作者： Goez, David Beyazit, Esra A. Slamnik-Krijestorac, Nina Marquez-Barja, Johann M. Gaviria, Natalia Latre, Steven Camelo, Miguel University of Antwerp - imec IDLab - Department of Computer Science Belgium Universidad de Antioquia Electronics Engineering Department Colombia University of Antwerp - imec IDLab - Faculty of Applied Engineering Belgium

ISBN: (纸本)9798331521110

The increasing demand for high-quality and efficient Channel Estimation (CE) in 5G New Radio (5G-NR) systems has prompted the exploration of advanced Deep Learning (DL) techniques. While traditional methods, such as Linear Interpolation (LI) and Least Squares (LS), provide reasonable accuracy and are practical for real-time physical layer processing, recent DL-based CE approaches have primarily focused on accuracy, often without evidence of real-time capabilities. In this paper, we present a comprehensive evaluation of DL-based Super-resolution (SR) methods for CE, comparing models like Super Resolution Convolutional Neural Network (SRCNN), ChannelNet, and Enhanced Deep Super-Resolution (EDSR) in both 1D and 2D convolutional architectures. We optimize these models using NVIDIA TensorRT to reduce computational complexity and latency. Our results show that the optimized 1D-EDSR model achieves the best performance with a Mean Squared Error (MSE) of 0.0126, outperforming all other models in terms of accuracy. However, the optimized 1D-EDSR model fails to meet real-time constraints due to additional computational overhead (0.6798 ms/sample). In contrast, the 1D-SRCNN model offers a balanced trade-off between MSE (0.01738) and inference time (0.0866ms/sample), achieving 40% higher accuracy than LS (0.0288) while maintaining the best energy efficiency (1.48 mJ/sample). © 2024 IEEE.

关键词： Mean square error

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4D Gaussian Splatting with Scale-aware Residual Field and Adaptive Optimization for Real-time Rendering of Temporally Complex Dynamic Scenes 24

4D Gaussian Splatting with Scale-aware Residual Field and Ad...

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32nd ACM International conference on Multimedia, MM 2024

作者： Yan, Jinbo Peng, Rui Tang, Luyang Wang, Ronggang School of Electronic and Computer Engineering Peking University Shenzhen China Pengcheng Laboratory Shenzhen China

ISBN: (纸本)9798400706868

Reconstructing dynamic scenes from video sequences is a highly promising task in the multimedia domain. While previous methods have made progress, they often struggle with slow rendering and managing temporal complexities such as significant motion and object appearance/disappearance. In this paper, we propose SaRO-GS as a novel dynamic scene representation capable of achieving real-time rendering while effectively handling temporal complexities in dynamic scenes. To address the issue of slow rendering speed, we adopt a Gaussian primitive-based representation and optimize the Gaussians in 4D space, which facilitates real-time rendering with the assistance of 3D Gaussian Splatting. Additionally, to handle temporally complex dynamic scenes, we introduce a Scale-aware Residual Field. This field considers the size information of each Gaussian primitive while encoding its residual feature and aligns with the self-splitting behavior of Gaussian primitives. Furthermore, we propose an Adaptive Optimization Schedule, which assigns different optimization strategies to Gaussian primitives based on their distinct temporal properties, thereby expediting the reconstruction of dynamic regions. Through evaluations on monocular and multi-view datasets, our method has demonstrated state-of-the-art performance. © 2024 ACM.

关键词： Rendering (computer graphics)

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Cluster-Mined Negative Samples for Enhanced Unsupervised Sentence Representation Learning 27th

Cluster-Mined Negative Samples for Enhanced Unsupervised Sen...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Zhang, Yuhang Zhang, Wenjie Hua, Yang Wang, Zun Song, Xiaoning Wu, Xiao-Jun School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Biotechnology Co. Ltd. Suzhou215000 China

ISBN: (纸本)9783031783944

Negative samples selection for contrastive learning is considerable in the field of sentence representation, especially for semantic textual similarity. Traditional in-batch negative sampling methods not only lack hard negative samples but also ignore potential false negative samples. Despite numerous methods trying to improve traditional sampling strategies, the challenge of consistently generating high-quality negative samples remains untackled. To address this pivotal issue, we propose the Cluster-Mined Negative Samples for Enhanced Unsupervised Sentence Representation Learning (CMNS) framework. Specifically, dynamic queues are utilized to store the K-means cluster samples, enabling the most appropriate selection of clusters to serve as negative samples. Additionally, we generate noise-based negative samples via stored clusters while simultaneously constraining potential false negative samples. Above all, CMNS employs clustering techniques to efficiently mine sufficient quantity of high-quality negative samples from unlabeled datasets. Extensive experiments illustrate that our approach not only overcomes the inherent limitations of traditional sampling methods but also improves the performance of sentence representations in downstream tasks, demonstrating measurable advancements over current methodologies. (codes and models are available at https://***/hamrain/CMNS). © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Contrastive Learning

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Dual-MambaNet: A Lightweight Dual-Branch Brain Image Segmentation Network Based on Local Attention and Mamba 27th

Dual-MambaNet: A Lightweight Dual-Branch Brain Image Segment...

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27th International conference on Pattern Recognition, ICPR 2024

作者： Zhang, Feifei Shi, Fei Ren, Dayong Jia, Zhenhong Wang, Jianyi School of Computer Science and Technology Xinjiang University Urumqi Xinjiang830046 China Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi Xinjiang830046 China National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

ISBN: (纸本)9783031781032

Brain tissue segmentation is critical for diagnosing and treating brain diseases. While Mamba-based models excel in the medical field, they face performance bottlenecks with high-resolution MRI images, often losing local feature information in complex texture structures. To address these challenges and enable deployment in resource-limited settings, we propose Dual-MambaNet, a lightweight segmentation model based on Mamba. In Dual-MambaNet, we introduce the Outlook attention module to capture local complex textures and structures in brain MRI images. Subsequently, we combined it with the Mamba block to construct a feature extractor (FE) encoder layer to couple local and global features. Additionally, we integrate dual decoder branches and a multi-level pixel contrastive loss function(MPCL) to better integrate local and global features. This method optimizes global feature representation by refining local complex textures and structural details, effectively capturing multi-level features in MRI images. Experimental results on public brain MRI datasets OASIS1 and MRBrainS13 demonstrate that Dual-MambaNet achieves high segmentation accuracy with minimal parameters and computational complexity, making it suitable for deployment in resource-limited medical environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Magnetic resonance imaging

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Proton-recoil spectrometer for fast neutron spectrum based on GEM gas detector 49

Proton-recoil spectrometer for fast neutron spectrum based o...

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49th EPS conference on Plasma Physics, EPS 2023

作者： Scholz, M. Dąbrowski, W. Wiącek, U. Jardin, A. Lach, B. Drozdowicz, K. Woźnicka, U. Kurowski, A. Kulińska, A. Mazon, D. KrakowPL-31-342 Poland AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland CEA IRFM Saint-Paul-lez-DuranceF-13108 France

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Reed-Solomon Codes over Ring with Lower Computational Complexity

Reed-Solomon Codes over Ring with Lower Computational Comple...

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2023 IEEE International conference on Big Data, BigData 2023

作者： Tan, Shicheng Hou, Hanxu Yu, Leilei Dongguan University of Technology School of Computer Science & Technology Dongguan China Dongguan University of Technology School of Electrical Engineering & Intelligentization Dongguan China University of Electronic Science & Technology of China Shenzhen Institute for Advanced Study China

ISBN: (纸本)9798350324457

Reed-Solomon (RS) codes are widely used in storage systems to provide high data reliability. The existing RS codes are constructed over finite field with field size larger than the code length that have high encoding/decoding complexity. In this paper, we propose a new construction for RS codes over a cyclic ring instead of finite field. Our new RS codes only incurs XOR and cyclic-shift operation in the encoding/decoding processes, and therefore have lower encoding/decoding complexity than the existing RS codes. In addition, we show that we can employ the efficient Reed-Muller (RM) transform in our new RS codes that reduces the encoding/decoding complexity. Moreover, we implement our new RS codes by C++ and show that our new RS codes have better encoding/decoding performance than the existing RS codes for the evaluated parameters. © 2023 IEEE.

关键词： cyclic ring cyclic-shift encoding/decoding complexity Reed-Muller transform Reed-Solomon codes

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MMFA-Net: Multi-Modal Feature Learning and Multi-Head Fuse Attention for Image Manipulation Detection and Localization 3

MMFA-Net: Multi-Modal Feature Learning and Multi-Head Fuse A...

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3rd International conference on Image Processing, computer Vision and Machine Learning, ICICML 2024

作者： Liu, Chengming Yao, Wenshuai Zhengzhou University School of Cyber Science and Engineering ZhengZhou China

ISBN: (纸本)9798350355413

With the advent of various advanced image processing techniques and the increasingly low manipulation threshold, Image Manipulation Detection and Localization (IMDL) has become a complex and challenging task. Previous studies have shown the importance of manipulation artifacts in the IMDL problem. However, existing methods often struggle to capture effective artifacts when addressing with refined forgery (post-processed forgery). In this paper, we introduce MMFA-Net, a novel approach for IMDL. We extract RGB features, Laplacian high-frequency features, Bayar statistical anomaly features, and manipulation imprint from the image through the Multi-modal Feature Extractor module, concatenating them as multi-modal feature inputs to capture manipulation edge artifacts, which are increasingly difficult to detect in image manipulation. In addition, we propose an innovative Multi-head Fuse Attention (MFA) module. Building on the backbone network's ability to model inconsistencies between original and tampered regions at coarse-to-fine granularities, the MFA module can more effectively capture contextual dependencies in the spatial dimensions and inter-channel correlations for more precise localization. The performance on several public datasets demonstrates that the proposed network outperforms existing SOTA models and remains robust against refined forgery. © 2024 IEEE.

关键词： computer Vision Deep Learning Image Feature Extraction Image Forensics

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Parallel Optimization of SLIC Algorithm for New-Generation Sunway Processors 23

Parallel Optimization of SLIC Algorithm for New-Generation S...

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7th International conference on high performance Compilation, Computing and Communications, HP3C 2023

作者： Xia, Zhuozhao Zhang, Qiang Zhou, Xinhang Zhao, Hongjie School of Computer Science Southwest Petroleum University Chengdu China

ISBN: (纸本)9781450399883

This study aims to explore optimization methods for superpixel segmentation algorithms in large-scale image processing. Due to the significant time consumption of existing superpixel segmentation algorithms when dealing with large-scale image data, this study focuses on parallel optimization of the SLIC superpixel segmentation algorithm on the new-generation Sunway manycore processor SW26010-Pro. The research analyzes the characteristics of the SW26010-Pro heterogeneous manycore processor, existing superpixel image segmentation algorithms, and the SLIC superpixel segmentation algorithm. The study primarily analyzes hot-spot functions and improves the algorithm's running speed by 6-7 times through optimizing processes such as RGB-to-CIELab color space conversion, cluster updating, and seed point updating. It effectively presents a parallel execution scheme for the SLIC superpixel algorithm on the SW26010-Pro. © 2023 ACM.

关键词： Superpixels

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