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Lay CO:Achieving Least Lossy Accuracy for Most Efficient RRAM-Based Deep Neural Network Accelerator via Layer-Centric Co-Optimization

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计算机科学技术学报（英文版） 2023年第2期38卷 328-347页

作者： Shao-Feng Zhao Fang Wang Bo Liu Dan Feng Yang Liu Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cloud Computing and Big Data Institute Henan University of Economics and LawZhengzhou 450001China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Research Institute of Huazhong University of Science and Technology in Shenzhen Shenzhen 518057China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou 450001China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and TechnologyWuhan 430074China School of Computer Science and Technology Huazhong University of Science and TechnologyWuhan 430074China Cloud Computing and Big Data Institute Henan University of Economics and LawZhengzhou 450001China

Resistive random access memory(RRAM)enables the functionality of operating massively parallel dot prod-ucts and ***-based accelerator is such an effective approach to bridging the gap between Internet of Things devices'constrained resources and deep neural networks'tremendous *** to the huge overhead of Analog to Digital(A/D)and digital accumulations,analog RRAM buffer is introduced to extend the processing in analog and in *** analog RRAM buffer offers potential solutions to A/D conversion issues,the energy consumption is still challenging in resource-constrained environments,especially with enormous intermediate data ***,criti-cal concerns over endurance must also be resolved before the RRAM buffer could be frequently used in reality for DNN in-ference *** we propose LayCO,a layer-centric co-optimizing scheme to address the energy and endurance con-cerns altogether while strictly providing an inference accuracy *** relies on two key ideas:1)co-optimizing with reduced supply voltage and reduced bit-width of accelerator architectures to increase the DNN's error tolerance and achieve the accelerator's energy efficiency,and 2)efficiently mapping and swapping individual DNN data to a correspond-ing RRAM partition in a way that meets the endurance *** evaluation with representative DNN models demonstrates that LayCO outperforms the baseline RRAM buffer based accelerator by 27x improvement in energy effi-ciency(over TIMELY-like configuration),308x in lifetime prolongation and 6x in area reduction(over RAQ)while main-taining the DNN accuracy loss less than 1％.

关键词： deep learning accelerator resistive random access memory (RRAM) energy efficiency error tolerance

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Efficient Approach for Virtual Machine and Resource Allocation in cloud computing 1

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5th International Conference on Information Systems and Management science, ISMS 2022

作者： Saxena, Rajat Dubey, Shatendra Sada, Rajesh Cloud Computing Lab Department of Computer Science and Engineering Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeth Vishwavidyalaya Indore India Cloud Computing Lab Department of Information Technology NRI Institute of Information Science and Technology Bhopal India

ISBN: (数字)9783031311536

ISBN: (纸本)9783031311529

Globalization has brought many changes in modern business world. Problems of enterprise oriented software applications like distribution and configuration of resources presents challenge to the traditional software sales model. cloud computing presents the solution for such problems. With the help of cloud-based model one can earn good return by making the QoS demands of their users (customers) satisfied. In order to fulfill the infrastructure demands (like network, storage, etc.) to their users, service providers have to maintain their own hardware or they lease it from the IaaS providers. For maintaining their own hardware SaaS providers will have to incur an extra cost, but if taken on lease, zero maintenance cost involved. Moreover, if provider wants to optimize cost and gain customer satisfaction, they will have to satisfy their customers/users by maintaining service level objectives. This paper presents a model for resource allocation in such a way as to minimize cost and maximize profit by satisfying the service level needs of the customers. The model is designed to enable the service providers with an ability to cope with the changing needs of customers, mapping customer requests to infrastructure level parameters and better handling of heterogeneous VMs. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Software as a service (SaaS)

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PSST: A Benchmark for Evaluation-driven Text Public-Speaking Style Transfer

PSST: A Benchmark for Evaluation-driven Text Public-Speaking...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Sun, Huashan Wu, Yixiao Ye, Yuhao Yang, Yizhe Li, Yinghao Li, Jiawei Gao, Yang School of Computer Science and Technology Beijing Institute of Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications China

ISBN: (纸本)9798891761681

Language style is necessary for AI systems to understand and generate diverse human language ***, previous text style transfer primarily focused on sentence-level data-driven approaches, limiting exploration of potential problems in large language models (LLMs) and the ability to meet complex application *** overcome these limitations, we introduce a novel task called Public-Speaking Style Transfer (PSST), which aims to simulate humans to transform passage-level, official texts into a public-speaking *** in the analysis of real-world data from a linguistic perspective, we decompose public-speaking style into key sub-styles to pose challenges and quantify the style modeling capability of *** such intricate text style transfer, we further propose a fine-grained evaluation framework to analyze the characteristics and identify the problems of stylized *** experiments suggest that current LLMs struggle to generate public speaking texts that align with human preferences, primarily due to excessive stylization and loss of semantic information. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Power Optimization for Low Transmission Delay in Software Defined Data Center Networks

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IEEE Transactions on Mobile computing 2025年第7期24卷 5716-5730页

作者： Zhao, Yong Cao, Jiannong Wang, Xingwei Li, Fuliang He, Qiang Liu, Xiaojie University of Electronic Science and Technology of China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Laboratory of Intelligent Collaborative Computing Chengdu611731 China Hong Kong Polytechnic University Department of Computing Hung Hom Hong Kong Northeastern University College of Computer Science and Engineering Shenyang110819 China Northeastern University College of Medicine and Biological Information Engineering Shenyang110819 China Pengcheng Laboratory Shenzhen518066 China

Software-Defined Data Center Networks (SDDCNs) utilizes Software Defined Networking (SDN) as a network architecture to achieve highly flexible, programmable, and automated management of Data Center Networks (DCNs). The high energy consumption of DCNs remains a persistent and significant challenge. Thus, the energy saving is crucial and imperative for DCNs. Current energy-efficient solutions primarily rely on flow consolidation and dynamic device sleeping techniques to reduce energy consumption. However, these approaches often yield long Flow Completion Time (FCT), potentially resulting in violations of service-level agreements, particularly for delay-sensitive applications. In this paper, we formulate the problem of minimizing the power consumption in SDDCNs as key objective while ensuring timely FCT for delay-sensitive applications. To solve this problem, we first introduce the Active Network Generation (ANG) approach, which generates a minimal active subnet with the least number of active devices while meeting the current traffic demand. Subsequently, we propose two algorithms based on the type of applications: the Delay-Tolerant Flow Route (DTFR) algorithm for delay-tolerant applications and the Delay-Sensitive Flow Route (DSFR) algorithm for delay-sensitive applications. Simulation results demonstrate that our propose solution achieves an energy-saving rate of up to 67.77% and significantly reduces FCT compared to benchmark solutions. © 2002-2012 IEEE.

关键词： Energy efficiency

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Generalized Multi-protocol Label Switching based Virtualization for cloud computing 1

Generalized Multi-protocol Label Switching based Virtualizat...

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1st International Conference on Connected Systems and Intelligence, CSI 2022

作者： Saxena, Rajat Patel, Ajay Singh Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Cloud computing Lab Department of Computer Science and Engineering Indore India Cloud computing Lab Department of Computer Science and Engineering Indore India

ISBN: (纸本)9781665458153

Generalized Multi Protocol Label Switching (GMPLS) or Multi-Protocol Lambda Switching is a new technology which produce amplification in Multi Protocol Label Switching (MPLS) to provide support network switching, space switching, and packet switching for time and wavelength. Thus, we can say that GMPLS is extension of MPLS which provides resilience and restoration by automatic switching. In this paper, we provide a Docker based simu-1ation environment which emulates a complex network and create insight functioning of GMPLS. The Docker based simulation environment is light-weight and scalable. It emulates large and complex network with minimal specifications. We have tested this Docker based GMPLS simulation testbed that depends on scaled network resources. Our method has shown tremendous improvement over the other virtualization methods. In this paper, we do a comparative study based on UML, Virtual Box, and Docker and found that Docker consumes very less resources when compared to UML and Virtual Box. In a system with 1 TB Hard disk space and 16 GB RAM, we can design and study a topology with 30-40 nodes using Virtual Box and a topology with 10-15 nodes using UML. Whereas, with Docker a topology with 300-nodes can be designed and tested. Thus scalability is limited by system specifications. © 2022 IEEE.

关键词： multi-protocol label switching

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Plan-on-Graph: Self-Correcting Adaptive Planning of Large Language Model on Knowledge Graphs 38

Plan-on-Graph: Self-Correcting Adaptive Planning of Large La...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Liyi Tong, Panrong Jin, Zhongming Sun, Ying Ye, Jieping Xiong, Hui University of Science and Technology of China China Alibaba Cloud Computing China Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Large Language Models (LLMs) have shown remarkable reasoning capabilities on complex tasks, but they still suffer from out-of-date knowledge, hallucinations, and opaque decision-making. In contrast, Knowledge Graphs (KGs) can provide explicit and editable knowledge for LLMs to alleviate these issues. Existing paradigm of KG-augmented LLM manually predefines the breadth of exploration space and requires flawless navigation in KGs. However, this paradigm cannot adaptively explore reasoning paths in KGs based on the question semantics and self-correct erroneous reasoning paths, resulting in a bottleneck in efficiency and effect. To address these limitations, we propose a novel self-correcting adaptive planning paradigm for KG-augmented LLM named Plan-on-Graph (PoG), which first decomposes the question into several sub-objectives and then repeats the process of adaptively exploring reasoning paths, updating memory, and reflecting on the need to self-correct erroneous reasoning paths until arriving at the answer. Specifically, three important mechanisms of Guidance, Memory, and Reflection are designed to work together, to guarantee the adaptive breadth of self-correcting planning for graph reasoning. Finally, extensive experiments on three real-world datasets demonstrate the effectiveness and efficiency of PoG. © 2024 Neural information processing systems foundation. All rights reserved.

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A Reduced State-Space Generation Method for Concurrent Systems Based on CPN-PR Model

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IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia University College of Computer Science the Engineering Research Center of Ecological Big Data Ministry of Education the Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software the Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model-checking method traverses the full state space, which suffers from the state-space explosion problem. A reduced state-space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state-space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model-checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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Local Context-Aware for Weakly Supervised Object Detection

Local Context-Aware for Weakly Supervised Object Detection

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2024 International Conference on Cyber-Physical Social Intelligence, ICCSI 2024

作者： Chen, Pengyu Nie, Xiushan Li, Tuo Bi, Yanchao Shandong Jianzhu University School of Computer Science and Technology Jinan China Shandong Yunhai Guochuang Cloud Computing Equipment Industry Innovation Co. Ltd. Jinan China

ISBN: (纸本)9798350376739

Weakly supervised object detection (WSOD) is designed to learn the class and location information of objects using image-level labels. Traditional WSOD approaches usually utilize Multiple-Instance Learning (MIL) to create numerous proposals and then choose the most distinctive ones as targets. However, methods that rely on convolutional neural networks tend to emphasize the most salient features of an object, often overlooking the complete object boundaries. In this paper, we propose a single-stage Local Context-Aware (LCA) based Transformer for WSOD. We employ category-wide patch mapping (CWPM) to take into account cross-patch information globally. We also incorporate the Local Awakening Module (LAM), which leverages local features to steer the model's learning process and enhance weak local responses. Comprehensive experiments on the VOC and COCO datasets showcase the efficacy of LCA. Meanwhile, LCA achieves much faster inference speed than the two-stage WSOD approaches. © 2024 IEEE.

关键词： Self-supervised learning

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Dependency-Type Weighted Graph Convolutional Network on End-to-End Aspect-Based Sentiment Analysis 13th

Dependency-Type Weighted Graph Convolutional Network on End...

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13th IFIP TC 12 International Conference on Intelligent Information Processing, IIP 2024

作者： Mu, Yusong Shi, Shumin School of Computer Science and Technology Beijing Institute of Technology Beijing China Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing China

ISBN: (纸本)9783031579189

Previous studies consider little on using dependency-type messages in the E2E-ABSA task. Studies using dependency-type messages just contact the dependency-type message and word embedding vectors, which may not fully fuse the context feature and information from the dependency type. This paper proposes a new model called Dependency-Type Weighted Graph Convolution Network (DTW-GCN) to compose dependency-type messages and word embedding. We use a type-weighted matrix to combine the dependency-type message, and DTW-GCN could fuse the dependency-type message and word embedding vectors. Experiments conducted on three benchmark datasets verify the effectiveness of our model. © IFIP International Federation for Information Processing 2024.

关键词： Sentiment analysis

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Elliptic Curve Diffie-Hellman based Privacy-Preserving Deduplication for Big Data in cloud Systems

Elliptic Curve Diffie-Hellman based Privacy-Preserving Dedup...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Madasu, Sairam Murugesan, Prakash Jaganathan, Humashankar Vellathur Pamulaparthyvenkata, Saigurudatta Masters of Science in Computer Science Cloud Computing and Artificial Intelligence United States Artificial Intelligence and Data Verizon United States Cbl Cgq Atlanta United States Big Data Independent Researcher United States

ISBN: (纸本)9798350360660

Data deduplication is crucial for cloud computing, particularly in managing the expansive growth of outsourced mobile data. Some existing approaches are vulnerable to brute-force attacks and single-point attacks, posing significant security risks. Furthermore, it causes substantial complexity on the resource-constrained edge nodes and the lack of support for cross-domain deduplication. To overcome this problem, this research aims to propose an effective and secure big data deduplication approach in cloud computing. This research proposes the novel combined encryption algorithm of the Elliptic Curve with Diffie Hellman (ECDH) for data privacy preservation and deduplication. The effectiveness and the security of the ECDH approach permit it to manage the enhanced data volumes in the multi-domain framework without significant performance degradation. The experimental results show that the proposed ECDH approach attains the effective communication overhead of data upload and data retrieval of 5.98Kb and 1.39Kb on the data size of 2Kb respectively as compared to the existing methods EC and DH. © 2024 IEEE.

关键词： Differential privacy

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