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Towards optimized tensor code generation for deep learning on sunway many-core processor

Frontiers of computer Science 2024年第2期18卷 1-15页

作者： Mingzhen LI Changxi LIU Jianjin LIAO Xuegui ZHENG Hailong YANG Rujun SUN Jun XU Lin GAN Guangwen YANG Zhongzhi LUAN Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China National University of Singapore Singapore 119077Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214000China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing 100854China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application *** the existing deep learning compilers,TVM is well known for its efficiency in code generation and optimization across diverse hardware *** the meanwhile,the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning *** paper combines the trends in these two ***,we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as *** addition,we leverage the architecture features during the compilation such as core group for massive parallelism,DMA for high bandwidth memory transfer and local device memory for data locality,in order to generate efficient codes for deep learning workloads on *** experiment results show that the codes generated by swTVM achieve 1.79x improvement of inference latency on average compared to the state-of-the-art deep learning framework on Sunway,across eight representative *** work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in *** believe this work will encourage more people to embrace the power of deep learning and Sunwaymany-coreprocessor.

关键词： sunway processor deep learning compiler code generation performance optimization

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An MCDM-Based Methodology for Influential Nodes Detection in a Social Network. Facebook as a Case Study

An MCDM-Based Methodology for Influential Nodes Detection in...

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International Conference on Advanced Intelligent systems for Sustainable Development, AI2SD 2022

作者： Qaffou, Issam Ait Rai, Khaoula LISI Laboratory Computer Science Department Faculty of Science Semlalia Cadi Ayyad University Marrakech Morocco Computer System and Vision Laboratory Faculty of Sciences Ibn Zohr University Agadir Morocco

ISBN: (纸本)9783031263835

The identification of influential nodes in a social network is very important for many uses like the control of information spreading. This paper studies how a multi- criteria decision method (MCDM) can find influential persons in a social network. The empirical study performed on a Facebook dataset shows how we can visualize links between profiles and who are the top-k influential ones. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Complex networks

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P3DC:Reducing DRAM Cache Hit Latency by Hybrid Mappings

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Journal of computer Science & Technology 2024年第6期39卷 1341-1360页

作者： Ye Chi Ren-Tong Guo Xiao-Fei Liao Hai-Kun Liu Jianhui Yue National Engineering Research Center for Big Data Technology and System Wuhan 430074China Services Computing Technology and System Laboratory Wuhan 430074China Cluster and Grid Computing Laboratory Wuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China School of Big Data and Internet Shenzhen Technology UniversityShenzhen 518118China Department of Computer Science Michigan Technological UniversityHoughton 49931-1295U.S.A.

Die-stacked dynamic random access memory(DRAM)caches are increasingly advocated to bridge the performance gap between the on-chip cache and the main *** fully realize their potential,it is essential to improve DRAM cache hit rate and lower its cache hit *** order to take advantage of the high hit-rate of set-association and the low hit latency of direct-mapping at the same time,we propose a partial direct-mapped die-stacked DRAM cache called *** design is motivated by a key observation,i.e.,applying a unified mapping policy to different types of blocks cannot achieve a high cache hit rate and low hit latency *** address this problem,P3DC classifies data blocks into leading blocks and following blocks,and places them at static positions and dynamic positions,respectively,in a unified set-associative *** also propose a replacement policy to balance the miss penalty and the temporal locality of different *** addition,P3DC provides a policy to mitigate cache thrashing due to block type *** results demonstrate that P3DC can reduce the cache hit latency by 20.5%while achieving a similar cache hit rate compared with typical set-associative caches.P3DC improves the instructions per cycle(IPC)by up to 66%(12%on average)compared with the state-of-the-art direct-mapped cache—BEAR,and by up to 19%(6%on average)compared with the tag-data decoupled set-associative cache—DEC-A8.

关键词： die-stacked dynamic random access memory(DRAM) cache set-associative direct-mapped hit latency

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Joint Task Allocation and Resource Optimization for Blockchain Enabled Collaborative Edge Computing

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China Communications 2024年第12期21卷 218-229页

作者： Xu Wenjing Wang Wei Li Zuguang Wu Qihui Wang Xianbin College of Electronic and Information Engineering Nanjing University of Aeronautics and AstronauticsNanjing 211106China Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information TechnologyNanjing 211106China Department of Electrical and Computer Engineering Western UniversityCanada

Collaborative edge computing is a promising direction to handle the computation intensive tasks in B5G wireless ***,edge computing servers(ECSs)from different operators may not trust each other,and thus the incentives for collaboration cannot be *** this paper,we propose a consortium blockchain enabled collaborative edge computing framework,where users can offload computing tasks to ECSs from different *** minimize the total delay of users,we formulate a joint task offloading and resource optimization problem,under the constraint of the computing capability of each *** apply the Tammer decomposition method and heuristic optimization algorithms to obtain the optimal ***,we propose a reputation based node selection approach to facilitate the consensus process,and also consider a completion time based primary node selection to avoid monopolization of certain edge node and enhance the security of the *** results validate the effectiveness of the proposed algorithm,and the total delay can be reduced by up to 40%compared with the non-cooperative case.

关键词： blockchain collaborative edge computing resource optimization task allocation

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A trajectory-level robot skill learning method based on fuzzy systems

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Science China(Information Sciences) 2025年第5期68卷 38-52页

作者： Chao ZENG Zhen DENG Boyang TI Yongsheng GAO Department of Computer Science University of Liverpool School of Mechanical Engineering and Automation Fuzhou University State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics and System Harbin Institute of Technology

Learning from demonstration(LfD) allows for the effective transfer of human manipulation skills to a robot by building a model that represents these skills based on a limited number of demonstrated ***,a skilllearning model that can comprehensively satisfy multiple requirements,such as computational complexity,modeling accuracy,trajectory smoothness,and robustness,is still ***,this work aims to provide such a model by employing fuzzy ***,we introduce an LfD model named Takagi-Sugeno-Kang fuzzy system-based movement primitives(TSKFMPs),which exploits the advantages of the fuzzy theory for effective robotic imitation learning of human *** work formulates the TSK fuzzy system and gradient descent(GD) as imitation learning models,leveraging recent advancements in GD-based optimization for fuzzy *** study takes a two-step strategy.(ⅰ) The input-output relationships of the model are established using TSK fuzzy systems based on demonstration *** this way,the skill is encoded by the model parameter in the latent space.(ⅱ) GD is used to optimize the model parameter to increase the modeling accuracy and trajectory *** further explain how learned trajectories are adapted to new task scenarios through local *** conduct multiple tests using an open dataset to validate our method,and the results demonstrate performance comparable with those of other ***,we implement it in a real-world case study.

关键词： robot skill learning movement primitives TSK fuzzy systems gradient descent robotics

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SpectrumChain: a disruptive dynamic spectrum-sharing framework for 6G

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Science China(Information Sciences) 2023年第3期66卷 26-39页

作者： Qihui WU Wei WANG Zuguang LI Bo ZHOU Yang HUANG Xianbin WANG College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Key Laboratory of Dynamic Cognitive System of Electromagnetic Spectrum Space Ministry of Industry and Information Technology No.8511 Research Institute of CASIC Department of Electrical and Computer Engineering Western University

The sixth-generation(6G) wireless network will support ubiquitous connectivity and diversified scenarios to satisfy the requirements of various emerging applications. Full spectrum is a key enabler for6G to achieve the ambitious goal of a Tbps-scale data rate. In this paper, we first review the scenario and potential spectrum plan for 6G and then focus on SpectrumChain, a blockchain-based dynamic spectrumsharing(DSS) framework for 6G. The unique characteristics of blockchain for DSS are presented along with key technologies. Finally, the conclusion and future development trends are discussed.

关键词： dynamic spectrum sharing blockchain 6G

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Age-of-information-aware PI controller for load frequency control

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Protection and Control of Modern Power systems 2023年第3期8卷 32-46页

作者： Dan Jiao Changzheng Shao Bo Hu Kaigui Xie Chengrong Lin Zhuoxuan Ju State Key Laboratory of Power Transmission Equipment and System Security Chongqing UniversityChongqing 400044China Department of Electrical and Computer Engineering George Washington UniversityDC 20052 WashingtonUSA

Open communication system in modern power systems brings concern about information staleness which may cause power system frequency *** information staleness is often characterized by communication ***,communication delay is a packet-centered metric and cannot reflect the requirement of information freshness for load frequency control(LFC).This paper introduces the age of information(AoI),which is more compre-hensive and informative than the conventional communication delay modeling *** LFC controller and com-munication are integrated into the design for LFC performance *** AoI-aware LFC model is formulated first,and considering each allowable update period of the smart sensor,different AoI-aware PI controllers are then designed according to the exponential decay *** right AoI-aware controller and update period are selected according to the degree of frequency fluctuation of the power *** studies are carried out on one-area and two-area power *** results show the superior performance of the AoI-aware controllers in comparison to the delay-dependent controllers.

关键词： Power system Load frequency control(LFC) Age of information(AoI) PI controller

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Detecting Pain During Cognitive Tasks via Visibility Graphs of Cortical Signals 22

Detecting Pain During Cognitive Tasks via Visibility Graphs ...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Subramanian, Ashwini Najafizadeh, Laleh Integrated System and NeuroImaging Laboratory Rutgers University Department of Electrical and Computer Engineering United States

ISBN: (纸本)9798331520526

Detecting pain is critical for developing adaptive systems in clinical and assistive settings, allowing for timely interventions. This work presents an approach to detect the presence of physical pain during the performance of cognitive tasks from cortical signals. Cortical signals from the motor and prefrontal cortices are acquired using functional near in-frared spectroscopy (fNIRS) during two cognitive tasks, per-formed in the absence and presence of physical pain. Visibil-ity graphs (VGs) of these signals are constructed, and graph metrics of averaged clustering coefficient (C) and edge den-sity (D) are extracted. Statistical analysis reveals differences in these metrics for graphs corresponding to tasks performed under no-pain (NP) versus with-pain (WP) conditions, partic-ularly for signals obtained from the motor cortex. The graph metrics were then used as features to a support vector machine (SVM), achieving an accuracy exceeding 80% in distinguishing between tasks performed with and without pain. Our findings suggest that metrics from VGs of cortical sig-nals may serve as potential biomarkers for pain during cog-nitive tasks. This approach could be especially beneficial for non-verbal patients using assistive brain-computer interfaces. © 2025 IEEE.

关键词： Cognitive tasks fNIRS Pain SVM Visibility graphs

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MLPFormer: MLP-integrated transformer for colorectal histopathology whole slide image segmentation

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Neural Computing and Applications 2025年第6期37卷 4651-4661页

作者： Wang, Yuxuan Li, Dan Li, Xuechen Guo, Yan Zuo, Yanfei Shen, Linlin Department of Computer Science Wenzhou-Kean University Wenzhou China School of Electronics and Information Engineering Wuyi University Jiangmen China Department of Histo Pathology Shanghai Histo Pathology Diagnostic Center Shanghai China College of Computer Sicence and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China

Colorectal intraepithelial neoplasia is a precancerous lesion of colorectal cancer, which is mainly diagnosed using pathological images. According to the characteristics of lesions, precancerous lesions can be classified into four different grades, i.e., background, normal, low and high level. However, due to the continuity of lesion development, tissue in different stages has high similarity. Recently, visual transformers have achieved impressive results in different visual tasks, due to their capacity of capturing long-range dependencies. However, due to computational cost, transformer cannot well extract detailed feature, which is important for lesion grading. To accurately segment pathological tissues with high similarity, in this work, we embed the multi-head MLP mixer module in the transformer network to extract cell and edge information. Furthermore, we propose a hierarchical MLP decoder to facilitate multi-scale feature fusion. The proposed transformer, namely MLPFormer, achieves remarkable results against competitive baselines on the Histo-CRC Biopsy dataset. The experimental results demonstrate that MLPFormer significantly outperforms the competitive baselines, i.e., a 3% dice improvement is achieved over the SegFormer series. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Image segmentation

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Benchmarking a ROS 2-Based Indirect SLAM on Edge Devices 4

Benchmarking a ROS 2-Based Indirect SLAM on Edge Devices

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4th International Conference on Robotics, Automation, and Artificial Intelligence, RAAI 2024

作者： Behret, Valentino Kushtanova, Regina Palme, Frank Munich University of Applied Sciences Laboratory for 3D Measuring Systems and Computer Vision Department of Mechanical Automotive and Aeronautical Engineering Munich Germany

ISBN: (纸本)9798331520038

This study provides insights into SLAM performance on embedded systems and introduces an unsupervised parameter tuning approach for enhanced localisation accuracy. Our paper benchmarks a ROS 2-based indirect Simultaneous Localisation and Mapping (SLAM) system across six NVIDIA Jetson devices: Orin Nano 4 GB, Orin Nano 8 GB, Orin NX 8 GB, Orin NX 16 GB, AGX Orin 32 GB Auvidea, and AGX Orin 32 GB Devkit, using sequences from the KITTI dataset. The edge devices differ significantly in GPU computing power, number of CPU cores, CPU clock frequency as well as memory. SLAM evaluations are conducted within Docker containers to ensure consistency, with feature detection parameters optimised using an unsupervised tuning approach for each device. In addition to localisation accuracy, hardware utilisation is analysed, revealing that less than a half of CPU capabilities and approximately 5- 10 % of GPU power are utilised. The results indicate that in the default configuration devices with lower computing power demonstrate higher fluctuation in SLAM localisation accuracy. However, optimising feature parameters improves accuracy stability, especially for less powerful edge devices, indicating that even lower-powered but more energy-saving devices can achieve similar performance when an optimised balance between feature quantity and runtime constraints is achieved. © 2024 IEEE.

关键词： Graphics processing unit

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