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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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PEGASIS - LEACH: Energy Efficient Protocols with Route Optimised Techniques 2

PEGASIS - LEACH: Energy Efficient Protocols with Route Optim...

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2nd International Conference for Advancement in Technology, ICONAT 2023

作者： Chauhan, Sandeep Nene, Manisha J. Defence Institute of Advanced Technology Computer Science and Engineering Department Pune India

ISBN: (纸本)9781665475174

The ad-hoc deployments of Wireless Sensor Network (WSN) in military operations is to gather information in critical areas where human interaction is limited. To improve the capability of WSN model there is a need to improve the power efficiency of the models. Power Efficient Gathering in Sensor Information Systems (PEGASIS) is tree based whereas Low-Energy Adaptive Clustering Hierarchy (LEACH) is clustering-based protocols which are used to improve the overall efficiency of ad-hoc WSN deployment and also increases the operational capability of the WSN *** paper aims to compare the results of PEGASIS and LEACH which is optimized with Particle swarm optimization (PSO) making it LEACH-PSO protocol. LEACH is optimized with PSO as it works on clustering as these techniques helps it to find the correct cluster head (CH) which will make it more efficient and also improves the overall deployment of the WSN architecture. PSO is stochastic optimizer technique based on the intelligence and movement of swarms therefore it assist in finding the correct CH. PEGASIS works on chain-based flow of information therefore it can be clubbed with ACO which will help in increasing the overall efficiency by identifying the correct leader Head (LH). PEGASIS AND LEACH when clubbed with route optimizing techniques such as Ant Colony Optimizer, PSO and Grey Wolf Optimizer has improved and effective WSN deployment. Simulation is done in MATLAB. © 2023 IEEE.

关键词： Wireless sensor networks

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Security on mobile cloud computing using cipher text policy and attribute based encryption scheme

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Materials Today: Proceedings 2023年 80卷 3059-3063页

作者： Swetha, Madireddy Latha, M. Department Of Computer Science & Engineering Vels Institute of Science Technology and Advanced Studies Tamil Nadu India Vels Institute of Science Technology & Advance Studies Tamil Nadu India

Mobile Cloud Computing (MCC) is a new paradigm that has been emerged by the advances in the Cloud Computing for Mobile devices to access Cloud services. The data security challenges against the data thefting, deleting, corrupting, or exploiting are existed as the data storage and access to/from the cloud has been most popular. In order to handle these data security issues in the cloud and to provide protection for the data, some innovative encryption methods have been developed. One of such encryption methods is Attribute-Based Encryption (ABE) that has a more control on the data in cloud as which is accessed by whom. Since from the recent years, many of academic researchers have paying a widespread of attention on using advanced cryptographic techniques to securely store, process, also share data over the untrusted cloud environment. One of the promising cryptographic techniques which can solve the open challenge of regulating the fine-grained access control of important data over the distributed cloud is the Cipher text Policy – Attribute Based Encryption (CP-ABE). Therefore, a survey of work is presented in this paper on different CP-ABE methods for the secure mobile cloud computing architecture. This paper presents some works that are focused on secure data storing, accessing and sharing using CP-ABE methods over the untrusted cloud environments. All the works that are studied are analyzed and compared with each other in the results to find the best out of it. © 2021

关键词： Mobile cloud computing

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Multiuser Computation Offloading for Long-Term Sequential Tasks in Mobile Edge Computing Environments

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Tsinghua science and Technology 2023年第1期28卷 93-104页

作者： Huanhuan Xu Jingya Zhou Wenqi Wei Baolei Cheng School of Computer Science and Technology Soochow UniversitySuzhou 215006China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125China Provincial Key Laboratory for Computer Information Processing Technology Soochow UniversitySuzhou 215006China School of Computer Science Georgia Institute of TechnologyAtlantaGA 30332USA

Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.

关键词： mobile edge computing sequential tasks computation offloading dependency

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Masked Siamese Prompt Tuning for Few-Shot Natural Language Understanding

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第2期5卷 624-633页

作者： Ni, Shiwen Kao, Hung-Yu Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518000 China National Cheng Kung University Department of Computer Science and Information Engineering Tainan701401 Taiwan

Recently, prompt-based learning has shown excellent performance on few-shot scenarios. Using frozen language models to tune trainable continuous prompt embeddings has become a popular and powerful methodology. For few-shot natural language understanding, even if we freeze the parameters of the pretrained language model, the learned pseudo-prompts might still be overfitted. In this article, we propose a novel masked Siamese prompt tuning (MSP-tuning) to improve few-shot natural language understanding. Concretely, MSP-tuning masks randomly out part of the prompt tokens to get a pair of masked Siamese prompts for each sample. Each training sample is then fed to the model twice with the masked Siamese prompts. Finally, MSP-tuning minimizes the Jensen-Shannon-divergence (JSD) between the two output probability distributions of the pretrained language model to regularize the model further. Experiment results on the few-shot GLUE benchmark and SuperGLUE benchmark show that MSP-tuning outperforms previous approaches. Numerically, our MSP-tuning achieves an average improvement of 1.79% (BERT-base) and 1.39% (BERT-large) of the GLUE benchmark and 1.90% (RoBERTa-base) and 1.71% (RoBERTa-large) of the SuperGLUE benchmark compared to state-of-the-art method P-tuning. Our method facilitates applying large pretrained language models in natural language understanding. © 2023 IEEE.

关键词： Bit error rate

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Bayesian Deep Learning for Dynamic Power System State Prediction Considering Renewable Energy Uncertainty

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Journal of Modern Power Systems and Clean Energy 2022年第4期10卷 913-922页

作者： Shiyao Zhang James J.Q.Yu Academy for Advanced Interdisciplinary Studies Southern University of Science and TechnologyShenzhen 518055China Department of Computer Science and Engineering Southern University of Science and TechnologyShenzhen 518055China

Modern power systems are incorporated with distributed energy sources to be environmental-friendly and ***,due to the uncertainties of the system integrated with renewable energy sources,effective strategies need to be adopted to stabilize the entire power ***,the system operators need accurate prediction tools to forecast the dynamic system states *** this paper,we propose a Bayesian deep learning approach to predict the dynamic system state in a general power ***,the input system dataset with multiple system features requires the data pre-processing ***,we obtain the dynamic state matrix of a general power system through the Newton-Raphson power flow ***,by incorporating the state matrix with the system features,we propose a Bayesian long short-term memory(BLSTM)network to predict the dynamic system state variables *** results show that the accurate prediction can be achieved at different scales of power systems through the proposed Bayesian deep learning approach.

关键词： Bayesian deep learning data analytics NewtonRaphson power flow renewable energy source system state

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A Comprehensive Multi-modal Approach for Enhanced Product Recommendations Based on Customer Habits

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Journal of The Institution of Engineers (India): Series B 2024年第6期105卷 1537-1545页

作者： Bodapati, Jyostna Devi Veeranjaneyulu, N. Yenduri, Lakshmana Kumar Advanced Computer Science and Engineering Vignan’s Foundation for Science Technology and Research Andhra Pradesh Guntur India Department of IT Vignan’s Foundation for Science Technology and Research Vadlamudi522213 India San Francisco Bay Area United States

In response to the evolving demands and challenges in recommendation systems, a comprehensive and innovative approach is proposed for delivering personalized and accurate product recommendations while mitigating existing limitations. This approach combines cutting-edge methodologies from collaborative filtering, content-based recommendation systems, and state-of-the-art deep learning architectures to enhance the standard of recommendations and improve user contentment. The hybrid approach encompasses various modules, including data preprocessing, feature engineering, deep learning model development, recommendation generation processing, and post-processing. Utilizing the H &M Fashion dataset, a rich collection of product images and metadata, for experimentation, the dataset undergoes preprocessing to ensure data quality, and features are extracted from both text and images using transfer learning approaches. These features are then integrated into a unified representation using fusion techniques. Subsequently, a deep learning model is developed to generate personalized recommendations based on the integrated features. Empirical findings on benchmark H &M dataset demonstrate the superiority of the proposed hybrid approach over traditional content-based and collaborative-based models, with improvements in accuracy, recall, and F1 score. The hybrid model effectively combines different recommendation techniques, offering robustness, adaptability, and enhanced recommendation quality. This research contributes to the advancement of recommendation systems, offering insights and solutions for delivering tailored recommendations in the fashion retail industry. © The Institution of Engineers (India) 2024.

关键词： Recommender systems

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Minimum Epsilon-Kernel Computation for Large-Scale Data Processing

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Journal of computer science & Technology 2022年第6期37卷 1398-1411页

作者： Hong-Jie Guo Jian-Zhong Li Hong Gao School of Computer Science and Technology Harbin Institute of TechnologyHarbin 150001China Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of SciencesShenzhen 518000China

Kernel is a kind of data summary which is elaborately extracted from a large *** a problem,the solution obtained from the kernel is an approximate version of the solution obtained from the whole dataset with a provable approximate *** is widely used in geometric optimization,clustering,and approximate query processing,etc.,for scaling them up to massive *** this paper,we focus on the minimumε-kernel(MK)computation that asks for a kernel of the smallest size for large-scale data *** the open problem presented by Wang et *** whether the minimumε-coreset(MC)problem and the MK problem can be reduced to each other,we first formalize the MK problem and analyze its *** to the NP-hardness of the MK problem in three or higher dimensions,an approximate algorithm,namely Set Cover-Based Minimumε-Kernel algorithm(SCMK),is developed to solve *** prove that the MC problem and the MK problem can be Turing-reduced to each ***,we discuss the update of MK under insertion and deletion operations,***,a randomized algorithm,called the Randomized Algorithm of Set Cover-Based Minimumε-Kernel algorithm(RA-SCMK),is utilized to further reduce the complexity of *** efficiency and effectiveness of SCMK and RA-SCMK are verified by experimental results on real-world and synthetic *** show that the kernel sizes of SCMK are 2x and 17.6x smaller than those of an ANN-based method on real-world and synthetic datasets,*** speedup ratio of SCMK over the ANN-based method is 5.67 on synthetic ***-SCMK runs up to three times faster than SCMK on synthetic datasets.

关键词： approximate query processing kernel large-scale dataset NP-hard

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Techno-economic analysis of green hydrogen as an energy-storage medium for commercial buildings

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Clean Energy 2023年第1期7卷 84-98页

作者： Rahul Rajeevkumar Urs Assia Chadly Ameena Al Sumaiti Ahmad Mayyas Department of Industrial and Systems Engineering Khalifa UniversityAbu Dhabi 127788UAE Department of Electrical Engineering and Computer Science Advanced Power and Energy CenterKhalifa UniversityAbu Dhabi 127788UAE

Green-hydrogen production is vital in mitigating carbon emissions and is being adopted *** its transition to a more diverse energy mix with a bigger share for renewable energy,United Arab Emirates(UAE)has committed to investing billions of dollars in the production of green *** study presents the results of the techno-economic assessment of a green-hydrogen-based commercial-building microgrid design in the *** microgrid has been designed based on the building load demand,green-hydrogen production potential utilizing solar photovoltaic(PV)energy and discrete stack reversible fuel cell electricity generation during non-PV *** the current market conditions and the hot humid climate of the UAE,a performance analysis is derived to evaluate the technical and economic feasibility of this *** study aims at maximizing both the building microgrid’s independence from the main grid and its renewable *** results indicate that the designed system is capable of meeting three-quarters of its load demand independently from the main grid and is supported by a 78%renewable-energy *** economic analysis demonstrates a 3.117-$/kg levelized cost of hydrogen production and a 0.248-$/kWh levelized cost for storing hydrogen as ***,the levelized cost of system energy was found to be less than the current utility costs in the *** analysis shows the significant impact of the capital cost and discount rate on the levelized cost of hydrogen generation and storage.

关键词： fuel cell electrolyser green hydrogen microgrid photovoltaic

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Design of a Magneto-Electric Monopole Antenna

Design of a Magneto-Electric Monopole Antenna

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2024 International Symposium on Antennas and Propagation, ISAP 2024

作者： Nakamura, Shunta Sano, Makoto Kuse, Ryuji Fukusako, Takeshi Department of Computer Science and Electrical Engineering Kumamoto University Kumamoto Japan Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan

ISBN: (纸本)9798350364774

This paper presents a compact magneto-electric (ME) monopole antenna. Based on the image theory, the lateral size of the conventional ME dipole antenna is reduced by half, by placing a vertical ground plane. The ME monopole antenna was designed at 2.5 GHz. High front-to-back ratio of 20 dB and low cross polarization are observed. © 2024 IEEE.

关键词： Monopole antennas

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