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International Symposium on Code Generation and Optimization (CGO)

作者： Guangli Li Jingling Xue Lei Liu Xueying Wang Xiu Ma Xiao Dong Jiansong Li Xiaobing Feng School of Computer Science and Technology University of Chinese Academy of Sciences China School of Computer Science and Engineering University of New South Wales Australia SKL of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China College of Computer Science and Technology Jilin University China

ISBN: (数字)9781728186139

ISBN: (纸本)9781728186146

Tensor-specialized hardware for supporting low-precision arithmetic has become an inevitable trend due to the ever-increasing demand on computational capability and energy efficiency in intelligent applications. The main challenge faced when accelerating a tensor program on tensor-specialized hardware is how to achieve the best performance possible in reduced precision by fully utilizing its computational resources while keeping the precision loss in a controlled manner. In this paper, we address this challenge by proposing QUANTENSOR, a new approach for accelerating general-purpose tensor programs by replacing its tensor computations with low-precision quantized tensor computations on NVIDIA Tensor Cores. The key novelty is a new residual-based precision refinement technique for controlling the quantization errors, allowing tradeoffs between performance and precision to be made. Evaluation with GEMM, deep neural networks, and linear algebra applications shows that QUANTENSOR can achieve remarkable performance improvements while reducing the precision loss incurred significantly at acceptable overheads.

关键词： Tensors Quantization (signal) Neural networks Market research Hardware Acceleration Optimization

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Semantic Communications for Future Internet: Fundamentals, Applications, and Challenges

arXiv

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arXiv 2022年

作者： Yang, Wanting Du, Hongyang Liew, Zi Qin Lim, Wei Yang Bryan Xiong, Zehui Niyato, Dusit Chi, Xuefen Shen, Xuemin Sherman Miao, Chunyan The Department of Communications Engineering Jilin University Changchun130012 China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore The School of Computer Science and Engineering The Energy Research Institute Ntu Interdisciplinary Nanyang Technological University Singapore Singapore Alibaba Group The Alibaba-NTU Joint Research Institute Nanyang Technological University Singapore Singapore School of Computer Science and Engineering Ntu Singapore Singapore The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The School of Computer Science and Engineering Nanyang Technological University Singapore Singapore The Alibaba-NTU Joint Research Institute Nanyang Technological University Singapore Singapore The Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly Nanyang Technological University Singapore Singapore

With the increasing demand for intelligent services, the sixth-generation (6G) wireless networks will shift from a traditional architecture that focuses solely on a high transmission rate to a new architecture that is based on the intelligent connection of everything. Semantic communication (SemCom), a revolutionary architecture that integrates user as well as application requirements and the meaning of information into data processing and transmission, is predicted to become a new core paradigm in 6G. While SemCom is expected to progress beyond the classical Shannon paradigm, several obstacles need to be overcome on the way to a SemCom-enabled smart wireless Internet. In this paper, we first highlight the motivations and compelling reasons of SemCom in 6G. Then, we provide an overview of SemCom-related theory development. After that, we introduce three types of SemCom, i.e., semantic-oriented communication, goal-oriented communication, and semantic-aware communication. Following that, we organize the design of the communication system into three dimensions, i.e., semantic information (SI) extraction, SI transmission, and SI metrics. For each dimension, we review existing techniques and discuss their benefits and limitation, as well as the remaining challenges. Then, we introduce the potential applications of SemCom in 6G and portray the vision of future SemCom-empowered network architecture. Finally, we outline future research opportunities. In a nutshell, this paper provides a holistic review of the fundamentals of SemCom, its applications in 6G networks, and the existing challenges and open issues with insights for further in-depth investigations. Copyright © 2022, The Authors. All rights reserved.

关键词： Network architecture

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Semantic communication meets edge intelligence

arXiv

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arXiv 2022年

作者： Yang, Wanting Liew, Zi Qin Lim, Wei Yang Bryan Xiong, Zehui Niyato, Dusit Chi, Xuefen Cao, Xianbin Letaief, Khaled B. Department of Communications Engineering Jilin University Changchun130012 China Information Systems Technology and Design Pillar Singapore University of Technology and Design Singapore Singapore Alibaba Group Alibaba-NTU Joint Research Institute Nanyang Technological University Singapore Singapore School of Computer Science and Engineering NTU Singapore Singapore School of Electronic and Information Engineering Beihang University Beijing China Hong Kong Hong Kong

The development of emerging applications, such as autonomous transportation systems, are expected to result in an explosive growth in mobile data traffic. As the available spectrum resource becomes more and more scarce, there is a growing need for a paradigm shift from Shannon's Classical Information Theory (CIT) to semantic communication (SemCom). Specifically, the former adopts a "transmit-before-understanding" approach while the latter leverages artificial intelligence (AI) techniques to "understand-before-transmit", thereby alleviating bandwidth pressure by reducing the amount of data to be exchanged without negating the semantic effectiveness of the transmitted symbols. However, the semantic extraction (SE) procedure incurs costly computation and storage overheads. In this article, we introduce an edge-driven training, maintenance, and execution of SE. We further investigate how edge intelligence can be enhanced with SemCom through improving the generalization capabilities of intelligent agents at lower computation overheads and reducing the communication overhead of information exchange. Finally, we present a case study involving semantic-aware resource optimization for the wireless powered Internet of Things (IoT). Copyright © 2022, The Authors. All rights reserved.

关键词： Resource allocation

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The research of the feature extraction and classification algorithm based on EEG signal of motor imagery

The research of the feature extraction and classification al...

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2019 International Conference on Virtual Reality and Intelligent Systems, ICVRIS 2019

作者： Zhang, Yingjie Li, Qi Yan, Hui Geng, Xiaozhong Department of Computer Technology and Engineering Changchun Institute of Technology Changchun Jilin Province130012 China Department of Computer Science and Technology School of Computer Science and Technology Changchun University of Science and Technology Changchun Jilin Province130022 China

ISBN: (纸本)9781728150505

BCI based on machine learning could makes use of the egg signals to communicate to output under the condition of without the participation of peripheral nerves and muscles. Extracting the essential features of the EEG signals in the presence of artifacts, training the classification algorithms and optimalizing the performance of classifier is critical procedure for BCI system. To some extent the BCI system can be treated as a pattern recognition system whose performance depends on both the features extraction and the features classification algorithm employed. Independent component analysis (ICA) can remove the artifact in the electroencephalogram (EEG) signal spontaneous evoked by left and right hand motor imagery and the classifier based on the Support Vector Machine (SVM) algorithm and on the common spatial pattern (CSP) algorithm apply the feature extracted from purified EEG signal to recognize and discriminate distinctive motor imagery pattern. © 2019 IEEE.

关键词： Independent component analysis

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PLC-based intelligent home control system

PLC-based intelligent home control system

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International Conference on Smart City and Intelligent Building, ICSCIB 2018

作者： Wei, Liming Wu, Yangyun Guo, Xiujuan School of Electrical and Computer Engineering Jilin Jianzhu University Changchun130118 China Art Science Architecture Creative Center Jilin Jianzhu University Changchun130118 China

ISBN: (纸本)9789811367328

This paper presents an intelligent home control system based on PLC. The system combines the BP neural network algorithm with the traditional PID algorithm. The proposed algorithm has better dynamic performance, faster adjustment speed, and smaller overshoot. It can track to control and adjust the complex and nonlinear data with a higher standard. At the same time, the overall design plan of the system is described in details. The system designed in this paper provides a good reference for the practical application of intelligent home. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Control systems

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A wearable 3D nanostructured Ni-MOF electrochemical sensor integrated with Janus fabric for sweat collecting and nutrients detecting

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Talanta 2026年 296卷

作者： Chaohuan Yang Shuyi Li Chenchen Liao Chengyu Du Hecheng Yao Yuchan Li Yuhan Zhang Urszula Stachewicz Yan Liu Key Laboratory of Bionic Engineering (Ministry of Education) Jilin University Changchun 130022 China School of Materials and Engineering Jilin University Changchun 130022 China Faculty of Metals Engineering and Industrial Computer Science AGH University of Krakow al. A. Mickiewicza 30 Krakow 30–059 Poland National Key Laboratory of Automotive Chassis Integration and Bionics Jilin University Changchun 130022 China Institute of Structured and Architected Materials Liaoning Academy of Materials Shenyang 110167 China

Detecting sweat nutrients provides significant information into metabolic cycling and health status. Rapid collection of sweat, prevention of sweat backflow, and avoidance of sweat contamination are essential for electrochemical detection of sweat composition. Herein, this study investigated a wearable sweat sensor which bridged the gap between sweat collection and sensitive electrochemical biosensing of sweat nutrients. Inspired by lotus leaf, the bioinspired Janus cotton fabric (PCL/Ag@CF) with asymmetric wettability showed unidirectional water transport (UWT) performance, water vapor permeability and antibacterial properties, which could create a dry and comfortable microenvironment on the skin surface. Meanwhile, the three-dimensional (3D) porous cauliflower-like nanostructured nickel metal-organic framework Ni 3 (HITP) 2 (Ni-MOF) composite was utilized to monitor ascorbic acid (AA) successfully, yet the Ni-MOF modified by glucose oxidase displayed good glucose sensing performance. Besides, the wearable sweat sensor including Janus fabric and electrochemical sensor detected AA and glucose in human sweat successfully, which illustrated the practical suitability for health monitoring. Thus, the wearable sweat sensor offered a potential approach for human health monitoring and point-of-care diagnosis using user-friendly technology.

关键词： Bioinspired Janus fabric Electrochemical sensor Metal organic frameworks Sweat sensing Unidirectional water transport

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ESAE: Evolutionary Strategy-Based architecture Evolution 14th

ESAE: Evolutionary Strategy-Based Architecture Evolution

引用

14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Gu, Xue Meng, Ziyao Liang, Yanchun Xu, Dong Huang, Han Han, Xiaosong Wu, Chunguo Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Changchun130012 China Zhuhai Laboratory of Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education School of Computer Zhuhai College of Jilin University Zhuhai519041 China Department of Electrical Engineering and Computer Science Informatics Institute and Christopher S. Bond Life Sciences Center University of Missouri ColumbiaMO65211 United States School of Software Engineering South China University of Technology GuangZhou510006 China

ISBN: (纸本)9789811534249

Although deep neural networks (DNNs) play important roles in many fields, the architecture design of DNNs can be challenging due to the difficulty of input data representation, the huge number of parameters and the complex layer relationships. To overcome the obstacles of architecture design, we developed a new method to generate the optimal structure of DNNs, named Evolutionary Strategy-based architecture Evolution (ESAE), consisting of a bi-level representation and a probability distribution learning approach. The bi-level representation encodes architectures in the gene and parameter levels. The probability distribution learning approach ensures the efficient convergence of the architecture searching process. By using Fashion-MNIST and CIFAR-10, the effectiveness of the proposed ESAS is verified. The evolved DNNs, starting from a trivial initial architecture with one single convolutional layer, achieved the accuracies of 94.48% and 93.49% on Fashion-MNIST and CIFAR-10, respectively, and require remarkably less hardware costs in terms of GPUs and running time, compared with the existing state-of-the-art manual screwed architectures. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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拓扑异质结(PbSe)_(5)(Bi_(2)Se_(3))_(6)高压诱导超导电性研究

引用

science China Materials 2023年第7期66卷 2822-2828页

作者：裴翠颖朱鹏李炳谈赵毅高玲玲李昌华朱世豪张庆华应天平谷林高波缑慧阳姚延荪孙建刘寒雨陈宇林王秩伟姚裕贵齐彦鹏 School of Physical Science and Technology ShanghaiTech UniversityShanghai 201210China Centre for Quantum Physics Key Laboratory of Advanced Optoelectronic Quantum Architecture and Measurement(MOE)School of PhysicsBeijing Institute of Technology Beijing 100081China Beijing Key Lab of Nanophotonics and Ultrafine Optoelectronic Systems Beijing Institute of TechnologyBeijing 100081China Material Science Center Yangtze Delta Region Academy of Beijing Institute of TechnologyJiaxing 314011China State Key Laboratory of Superhard Materials and International Center for Computational Method and Software College of PhysicsJilin UniversityChangchun 130012China International Center of Future Science Jilin UniversityChangchun 130012China 7 Beijing National Laboratory for Condensed Matter PhysicsInstitute of PhysicsChinese Academy of SciencesBeijing 100190China Center for High Pressure Science and Technology Advanced Research Beijing 100094China Department of Physics and Engineering Physics University of SaskatchewanSaskatoonSaskatchewan S7N 5E2Canada National Laboratory of Solid State Microstructures School of Physics and Collaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093China ShanghaiTech Laboratory for Topological Physics ShanghaiTech UniversityShanghai 201210China Department of Physics Clarendon LaboratoryUniversity of OxfordParks RoadOxford OX13PUUK Shanghai Key Laboratory of High-resolution Electron Microscopy ShanghaiTech UniversityShanghai 201210China

最近,天然异质结(PbSe)_(5)(Bi_(2)Se_(3))_(6)在理论上预测并实验证实为拓扑绝缘体.本文通过高压原位量子调控在(PbSe)_(5)(Bi_(2)Se_(3))_(6)中成功引入超导电性.当压力增加到10 GPa时,超导电性突然出现,T_(c)约为4.6 K,随后T_(c)急... 详细信息

最近,天然异质结(PbSe)_(5)(Bi_(2)Se_(3))_(6)在理论上预测并实验证实为拓扑绝缘体.本文通过高压原位量子调控在(PbSe)_(5)(Bi_(2)Se_(3))_(6)中成功引入超导电性.当压力增加到10 GPa时,超导电性突然出现,T_(c)约为4.6 K,随后T_(c)急剧下降.值得注意的是,当进一步施加压力到30 GPa以上时,出现了一种新的超导态,且T_(c)直到本实验最高压力仍未饱和.结合XRD和拉曼光谱,我们认为(PbSe)_(5)(Bi_(2)Se_(3))_(6)中两个超导态的出现与压力诱导的结构相变有关.拓扑异质结压力下出现新的量子态,为进一步研究拓扑和超导的关系提供了一个良好的平台.

关键词： high pressure superconductivity heterostructure topological material

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Subwavelength field localization based on dielectric mesoscale particle with single and blind nanohole array

arXiv

引用

arXiv 2022年

作者： Minin, Oleg V. Zhou, Song Cao, Yinghui Baranov, Pavel Minin, Igor V. Tomsk Polytechnic University 30 Lenin Ave. Tomsk634050 Russia Jiangsu Key Laboratory of Advanced Manufacturing Technology Faculty of Mechanical and Material Engineering Huaiyin Institute of Technology Huaian223003 China College of Computer Science and Technology Jilin University 2699 Qianjin Street Changchun130021 China

Some new unusual physical phenomena and effects associated with dielectric mesoscale particles with Mie size parameter near 10 were studied and have been discovered during the last decade. In this paper, we propose nanoholes structured wavelength-scaled dielectric cubic particle with refractive index near two, where the array of nanoholes can act as a plurality of near-field probes to simultaneously illuminate the sample surface and it has the potential of surpassing the performance of most existing nearfield imaging approaches. We also offer the concept of the single nano-structuring of a dielectric cylinder or sphere made from conventional optical materials. The choice of the diameter of the nanohole in the particle "transfers" it into the resonance mode, when the characteristics of the field localized in the shadow part of the particle are determined not by the wavelength, but by the size of the nanohole. Thus, the diameter of the focused spot at the exit from the particle can be much smaller than the solid immersion diffraction *** Codes 65Z05 © 2022, CC BY.

关键词： Dielectric materials

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Understanding the Runtime Overheads of Deep Learning Inference on Edge Devices

Understanding the Runtime Overheads of Deep Learning Inferen...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Xiu Ma Guangli Li Lei Liu Huaxiao Liu Xiaobing Feng College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China SKL of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China

With the growing ubiquity of the Internet of Things, in-the-edge inference of deep neural network models has been a major driver for promoting the widespread use of intelligent applications. As model inference characteristics are crucial for optimizing and deploying deep neural networks on hardware platforms, many studies focus on analyzing the performance of neural networks such as latency, accuracy, throughput, and energy consumption. However, few existing works have ever discussed the runtime overheads hidden in neural network inference, despite the overheads are non-negligible for edge applications. The lack of in-depth analysis of the overheads hinders the understanding of how hardware designs and model structures impact on-device inference performance. In this paper, we characterize the runtime overheads of deep learning inference on representative edge devices by leveraging state-of-the-art neural network models, performing a systematical analysis from the perspective of end-to-end performance, hardware platforms, memory bandwidth, and neural network model structures. Based on experimental results, the crucial insights are offered to facilitate the design and configure of resource-efficient networks and pick appropriate models on the specific platform, which provides a comprehensive view of runtime overheads of in-the- edge neural network inference for architects and developers.

关键词： Deep learning Performance evaluation Analytical models Runtime Roads Neural networks Throughput

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