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research progress of quantum artificial intelligence in smart city

Intelligent and Converged Networks 2024年第2期5卷 116-133页

作者： Sumin Wang Ning Wang Tongyu Ji Yiyun Shi Chao Wang the Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai UniversityShanghai 200444China School of Information Engineering Ganzhou Key Laboratory of Cloud Computing and Big Data ResearchGannan University of Science and TechnologyGanzhou 341000China

The rapid accumulation of big data in the Internet era has gradually decelerated the progress of Artificial Intelligence(AI).As Moore’s Law approaches its limit,it is imperative to break the constraints that are holding back artificial *** computing and artificial intelligence have been advancing along the highway of human civilization for many years,emerging as new engines driving economic and social *** article delves into the integration of quantum computing and artificial intelligence in both research and *** introduces the capabilities of both universal quantum computers and special-purpose quantum computers that leverage quantum *** discussion further explores how quantum computing enhances classical artificial intelligence from four perspectives:quantum supervised learning,quantum unsupervised learning,quantum reinforcement learning,and quantum deep *** an effort to address the limitations of smart cities,this article explores the formidable potential of quantum artificial intelligence in the realm of smart *** does so by examining aspects such as intelligent transportation,urban operation assurance,urban planning,and information communication,showcasing a plethora of practical achievements in the *** the foreseeable future,Quantum Artificial Intelligence(QAI)is poised to bring about revolutionary development to smart *** urgency lies in developing quantum artificial intelligence algorithms that are compatible with quantum computers,constructing an efficient,stable,and adaptive hybrid computing architecture that integrates quantum and classical computing,preparing quantum data as needed,and advancing controllable qubit hardware equipment to meet actual *** ultimate goal is to shape the next generation of artificial intelligence that possesses common sense cognitive abilities,robustness,excellent generalization capabilities,and interpretability.

关键词： quantum artificial intelligence smart city D-Wave quantum computer

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A reposition algorithm for e-hailing based on quantum annealing and intuitive reasoning

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Intelligent and Converged Networks 2024年第4期5卷 317-335页

作者： Chao Wang Yiyun Shi Sumin Wang Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai UniversityShanghai 200444China School of Information Engineering Ganzhou Key Laboratory of Cloud Computing and Big Data ResearchGannan University of Science and TechnologyGanzhou 341000China

Currently,the challenge lies in the traditional intelligent algorithm’s ability to effectively address the e-hailing repositioning *** identifying the underlying characteristics in extensive traffic data within a limited timeframe is difficult,ultimately preventing the achievement of the most optimal *** paper suggests a hybrid computing architecture involving reinforcement learning and quantum annealing based on intuitive *** reasoning aims to enhance performance in scenarios with poor system robustness,complex tasks,and diverse goals.A deep learning model is constructed,trained to extract scene features,and combined with expert knowledge,then transformed into a quantum annealable *** final strategy is obtained using a D-wave quantum computer with quantum tunneling effect,which helps in finding optimal solutions by jumping out of local suboptimal *** on 400000 real data,four algorithms are compared:minimum-cost flow,sequential markov decision process,hot-dot strategy,and driver-prefer *** average total revenue increases by about 10%and vehicle utilization by about 15%in various *** summary,the proposed architecture effectively solves the e-hailing reposition problem,offering new directions for robust artificial intelligence in big data decision problems.

关键词： intuitive reasoning reinforcement learning quantum annealing

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Runtime reconfiguration of data services for dealing with out-of-range stream fluctuation in cloud-edge environments

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Digital Communications and Networks 2022年第6期8卷 1014-1026页

作者： Shouli Zhang Chen Liu Xiaohong Li Yanbo Han Division of Intelligence and Computing Tianjin UniversityTianjin300072China Beijing Key Laboratory on Integration and Analysis of Large-Scale Stream Data North China University of TechnologyBeijing10009China Cloud Computing Research Center North China University of TechnologyBeijing10009China

The integration of cloud and IoT edge devices is of significance in reducing the latency of IoT stream data processing by moving services closer to the *** this connection,a key issue is to determine when and where services should be *** service deployment strategies used to be static based on the rules defined at the design ***,dynamically changing IoT environments bring about unexpected situations such as out-of-range stream fluctuation,where the static service deployment solutions are not *** this paper,we propose a dynamic service deployment mechanism based on the prediction of upcoming stream *** effectively predict upcoming workloads,we combine the online machine learning methods with an online optimization algorithm for service deployment.A simulation-based evaluation demonstrates that,compared with those state-of-the art approaches,the approach proposed in this paper has a lower latency of stream processing.

关键词： IoT stream processing Edge computing Out-of-Range stream fluctuation Dynamic service deployment

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Some Thoughts on the Classification Standard of Intelligent Autonomous Level of Measurement and Control System 9

Some Thoughts on the Classification Standard of Intelligent ...

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9th International Conference on Big Data and Information Analytics, BigDIA 2023

作者： Yuan, Guangfu Yang, Liu Han, Haoran Li, Yong Shao, Cong Zhan, Siyu Gansu Photoelectric Measurement Technology Research Laboratory Gansu735018 China University of Electronic Science and Technology of China Laboratory of Intelligent Collaborative Computing Chengdu China Trusted Cloud Computing and Big Data Key Laboratory of Sichuan Province Chengdu611731 China

ISBN: (纸本)9798350330076

Intelligence is an important trend in the development of measurement and control systems. However, there is still a lack of unified understanding of the intelligent autonomous classification of the measurement and control system. The establishment of intelligent evaluation standards in this field has become an urgent research topic. This paper first gives an overview of the aviation measurement and control system, and then takes the classification standards of autonomy in the fields of automobile driving automation in China and the United states, the autonomy and autonomy level of typical ships in China, and the automation of civil unmanned aircraft system in China as a reference. A 5-level intelligent grading standard based on task adaptability, environmental complexity and human-computer interaction is proposed. The classification standard is practical and easy to understand. It can provide reference for the establishment of intelligent standards for measurement and control systems, improve the application speed of intelligent autonomous technology in the field of measurement and control, and broaden the application prospect of intelligent measurement and control systems. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Multi-source Autoregressive Entity Linking Based on Generative Method 18th

Multi-source Autoregressive Entity Linking Based on Generati...

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18th CCF Conference on Computer Supported Cooperative Work and Social computing, ChineseCSCW 2023

作者： Yang, Dongju Lan, Weishui Beijing Municipal Key Laboratory on Integration and Analysis of Large-Scale Stream Beijing100144 China Data Research Center for Cloud Computing North China University of Technology Beijing100144 China

ISBN: (纸本)9789819996360

The task of entity linking aims to map the entity reference in the text with the unambiguous entity in the knowledge base. However, under the background of the continuous growth of science and technology service platform services, the complexity and diversity of domain semantic information, and the vagueness and ambiguity of natural language, the task of linking industrial chain knowledge graphs and technology service resources faces the following problems: (1) The increase in the types of semantic information in the do-main will lead to the lack of cognition and ambiguity in the entity linking process, which will affect the accuracy of entity linking;(2) the context and interaction of corpus information Dependencies become more complex, making insufficient consideration of the mapping relationship between linked entities. To address the above issues, this paper proposes a generative method and multiple sources of information Autoregressive Entity Linking (GMoAEL), which uses data sets such as unstructured textual descriptions, related reference links, and fine-grained structured types Information and other sources of information are used to build a unified dense representation for entity learning, and an autoregressive model in the form of encoder-decoder is used to adjust the link generation process to handle the complex mapping relationship between in-put and output. This paper uses the AIDA CoNLL-YAGO data set to conduct experiments. Compared with other methods, the Micro-F1 value is 1.6% points ahead, which verifies the feasibility and effectiveness of this method. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Knowledge graph

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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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Accelerating Blake3 in RISC-V 2

Accelerating Blake3 in RISC-V

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2nd International Conference on computing, Communication, Perception and Quantum technology, CCPQT 2023

作者： Zou, Xiaofeng Peng, Yuanxi Li, Tuo Kong, Lingjun Pang, Zhaochun Zhang, Lu National University of Defense Technology Institute for Quantum Information State Key Laboratory of High Performance Computing Changsha China Cloud Computing Equipment Innovation Center Second Research Institute Jinan China

ISBN: (纸本)9798350342697

This paper proposes a novel hardware design to accelerate the Blake3 hash algorithm, a cryptographic hash function, on RISC-V architecture, using both vector extension (V1.0) and a specially designed Blake3 instruction. The design leverages the parallelism and simplicity of BLAKE3 to achieve high throughput and low latency, implementing the core compression function using a custom instruction set architecture that supports vector operations and constant computing. The Blake3 compression function is speed up in architecture based on hardware/software co-design. The design achieves a speed-up of 10 times for the compression function compare with a single core of 2.7GHz Xeon 8280 and 10% faster than an AVX512 implementation. © 2023 IEEE.

关键词： Hash functions

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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 2024年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou 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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Design and Implementation of a Compiler Supporting RISC-V Custom Cryptographic and Vector Instructions 23

Design and Implementation of a Compiler Supporting RISC-V Cu...

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7th International Conference on Computer Science and Application Engineering, CSAE 2023

作者： Zou, Xiaofeng Peng, Yuanxi Zhang, Qing Institute for Quantum Information State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China Second Research Department Cloud Computing Equipment Industry Innovation Center Jinan China

ISBN: (纸本)9798400700590

Post-quantum cryptographic algorithms capable of resisting quantum computing attacks have received increasing attention in recent years, especially lattice-based cryptographic algorithms have become mainstream. Since their specialization and complex computation, the cryptographic computation is inefficient on general-purpose processors. In order to improve the performance of the processor in post-quantum cryptography computing, we have extended a set of cryptographic and vector instruction sets for the CRYSTALS-Kyber algorithm, and implemented support for custom cryptographic and vector instructions based on the open-source toolchain. These efforts can realize the compilation and disassembly of the CRYSTALS-Kyber encryption/decryption algorithm programs, and optimized the performance of the compilation. This paper mainly introduces how to design an extended instruction set based on the target algorithm and enable the compiler to support these instructions. Compared to the situation before extending custom instructions, the total number of instructions for key generation, encryption, and decryption assembly programs has decreased by 3651 times, and the total number of execution cycles has decreased by 2195 times. © 2023 ACM.

关键词： Program compilers

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Scaling Inter-procedural Dataflow Analysis on the cloud

arXiv

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arXiv 2024年

作者： Sun, Zewen Zhang, Yujin Xu, Duanchen Zhang, Yiyu Qi, Yun Wang, Yueyang Li, Yi Wang, Zhaokang Li, Yue Li, Xuandong Zuo, Zhiqiang Lu, Qingda Peng, Wenwen Guo, Shengjian State Key Laboratory for Novel Software Technology Nanjing University China Alibaba Cloud Computing United States Alibaba Cloud Computing China Baidu Research United States

Apart from forming the backbone of compiler optimization, static dataflow analysis has been widely applied in a vast variety of applications, such as bug detection, privacy analysis, program comprehension, etc. Despite its importance, performing interprocedural dataflow analysis on large-scale programs is well known to be challenging. In this paper, we propose a novel distributed analysis framework supporting the general interprocedural dataflow analysis. Inspired by large-scale graph processing, we devise dedicated distributed worklist algorithms for both whole-program analysis and incremental analysis. We implement these algorithms and develop a distributed framework called BigDataflow running on a large-scale cluster. The experimental results validate the promising performance of BigDataflow - BigDataflow can finish analyzing the program of millions lines of code in minutes. Compared with the state-of-the-art, BigDataflow achieves much more analysis efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Data flow graphs

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