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Preliminary Study on Reducing Memory Overhead in Accelerating Quantum Computer Simulations Using PIM technology 20

Preliminary Study on Reducing Memory Overhead in Acceleratin...

引用

20th international SoC Design conference (ISOCC)

作者： Hour, Leanghok Go, Myeongseong Han, Youngsun Pukyong Natl Univ Dept Artificial Intelligence Convergence Busan South Korea

ISBN: (纸本)9798350327038

Recent developments in quantum algorithms have demonstrated the promise of quantum computers, even in the era of noisy intermediate-scale quantum computing (NISQ). However, accessing real quantum computers remains challenging due to cost and long wait times. Additionally, the limited scale and noise of these computers can impact the outcomes of algorithms. To overcome these challenges, researchers often rely on quantum computer simulators. However, these simulators, which use CPUs and/or GPUs, suffer from high memory overhead. To address this issue, we propose a processing-in-memory (PIM)-based technology for quantum computer simulation. We confirmed that our proposed approach reduces data movement cycles in memory by 2.76 times on average compared to traditional execution on CPUs.

关键词： Quantum computer simulation processing-inmemory

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UAV-Assisted Integrated Sensing and Communication for Emergency Rescue Activities Based on Transfer Deep Reinforcement Learning 30

UAV-Assisted Integrated Sensing and Communication for Emerge...

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30th international conference on Mobile computing and Networking, ACM MobiCom 2024

作者： Liu, Yaxi Mao, Wencan Li, Xulong Huangfu, Wei Ji, Yusheng Xiao, Yu University of Science and Technology Beijing Beijing China National Institute of Informatics Tokyo Japan Aalto University Espoo Helsinki Finland

ISBN: (纸本)9798400704895

Joint task scheduling and resource allocation for unmanned aerial vehicle (UAV)-assisted integrated sensing and communication (ISAC) in emergency rescue activities has become an essential and challenging problem. However, the existing works have only considered such a problem for standalone UAV networks without considering the cooperation between UAVs and ground base stations (BSs), nor have they considered the uncertainty in terms of the availability of BSs due to damage/reconstruction in disaster events. In this paper, we consider a novel post-disaster UAV-assisted ISAC system where the UAVs are used to supplement the networking capacity of out-of-service ground BSs while using their radio signals for sensing. We apply transfer learning with deep reinforcement learning (DRL) to learn task scheduling and resource allocation strategies that can rapidly adapt to uncertainty in the environment. Experimental results show that the proposed algorithm outperforms the state-of-the-art in both communication and sensing performance and convergence speed. Moreover, the transfer learning-based DRL shows faster convergence and better robustness when the availability of BSs suddenly changes. © 2024 Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Deep reinforcement learning

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Conjugate Momentum Quadratic Penalty Alternating Minimization for Total Variation Image Restoration

Conjugate Momentum Quadratic Penalty Alternating Minimizatio...

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2024 IEEE international conference on Systems, Man, and Cybernetics, SMC 2024

作者： Ren, Ong Yin Adam, Tarmizi Mohamed, Nur Syarafina Hassan, Mohd Fikree Yong, Pang Yee Universiti Teknologi Malaysia Faculty of Computing Johor Skudai81310 Malaysia Universiti Teknologi Malaysia Faculty of Sciences Department of Mathematical Sciences Johor Skudai81310 Malaysia School of Information Technology Monash University Malaysia Selangor Subang Jaya47500 Malaysia

ISBN: (纸本)9781665410205

Optimization algorithms are a key tool in image restoration. The Quadratic Penalty Alternating Minimization (QPAM) algorithm is an algorithm used to tackle image restoration challenges. However, the persistent challenge of slow convergence speed remains. Efforts have been made to enhance convergence speed, including extending the algorithm with Nesterov's momentum method. Yet, the algorithm displays oscillatory patterns during the minimization process, which may result in slow convergence speed. To address this issue, we proposed a conjugate gradient style momentum to accelerate the QPAM for image restoration. The iterative scheme of the proposed method consists of a proximal linearization that is re-formulated for the conjugate momentum acceleration. Experiments on both Gaussian and Poisson noise image restoration show that our proposed Conjugate Momentum QPAM is at par with or better than the original QPAM and its Nesterov-accelerated version in terms of CPU time. © 2024 IEEE.

关键词： Image reconstruction

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Enhanced Genetic Algorithm for Variable Minimization through Modified Crossover Operator 7

Enhanced Genetic Algorithm for Variable Minimization through...

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7th international conference on Electronics, Communication and Aerospace technology, ICECA 2023

作者： Bustillo, James Cloyd M. College of Computing and Information Sciences Agusan Del sur State College of Agriculture and Technology Bunawan Philippines

ISBN: (纸本)9798350340600

Genetic algorithms (GAs) are commonly employed in optimization techniques and one of the crucial components of GAs is the crossover function, which often encounters the issue of premature convergence (PC). The proposed innovative crossover operator, known as Flip Multi-Sliced Average Crossover (FMSAX), significantly enhances the performance of GA by addressing variable minimization. This approach introduces a novel method of generating offspring by dividing the chromosomes into three equal segments, producing a head-body-tail structure. This is achieved by flipping each gene and computing the average to generate a new offspring. The FMSAX operator represents a more optimized and efficient technique for variable optimization, aimed at mitigating premature convergence issues. The simulation results, two types of datasets, each containing 30 and 40 variables, were utilized. The results clearly demonstrate that the enhanced crossover operator within the GA outperformed the original GA with an average crossover operator by 25 variables (62.50%) and 10 variables (25%) eliminated, respectively. Moreover, it yielded Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) values of 0.0066 and 0.0738, respectively. © 2023 IEEE.

关键词： Genetic algorithms

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Improved Multilayer Perceptron Neural Networks Weights and Biases Based on The Grasshopper optimization Algorithm to Predict Student Performance on Ambient Learning 23

Improved Multilayer Perceptron Neural Networks Weights and B...

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7th international conference on Machine Learning and Soft computing, ICMLSC 2023

作者： Michira, Mercy K. Rimiru, Richard M. Mwangi, Waweru R. School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Kenya School of Computing and Information Jomo Kenyatta University of Agriculture and Technology Kenya

ISBN: (纸本)9781450398633

The classification accuracy of a multi-layer Perceptron Neural Networks depends on the selection of its parameters such the connection weights and biases. Generating an optimal value of these parameters requires a suitable algorithm to train the multilayer perceptron neural networks. This paper presents swam based Grasshopper optimization algorithm that optimizes the connection weights and biases of Multilayer Perceptron Neural Network. Grasshopper optimization algorithm is a swarm-based metaheuristic algorithm applied for accurate learning of Multilayer Perceptron Neural Networks. The proposed Multilayer Layer Perceptron Neural Networks based on the Grasshopper Optimization Algorithm was validated using a Genetic algorithm and Backpropagation algorithm this algorithm has proved to perform satisfactorily performance by escaping local optimal and its fast convergence. © 2023 Owner/Author.

关键词： Multilayer neural networks

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Mobile Robot Navigation in an Unknown Environment Based on Deep Reinforcement Learning 5

Mobile Robot Navigation in an Unknown Environment Based on D...

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5th international conference on Intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Wang, Zhiyu Wang, Minghui Hong Kong Polytechnic University Dept. Computing Hong Kong Guangdong University of Technology Dept. Computer Science Guangzhou China

ISBN: (纸本)9798350368284

This paper studies an end-To-end navigation method based on deep reinforcement learning, which enables mobile robots to independently explore and navigate in unknown environments using only sensor data and without map information. In this paper, we propose an improved deep reinforcement learning method, design a deep reinforcement learning (DRL) optimization model, optimize the experience pool to improve the utilization rate of valuable positive reward transfer samples, and introduce a goal orientation strategy to help the robot avoid blind exploration. Experimental results show that the proposed optimization model could realize path planning and dynamic obstacle avoidance for mobile robots in complex environments. The convergence speed, planning success rate, and path precision were improved to a certain extent. Compared with the traditional DRL method, the robot's exploration efficiency was higher, and the learned strategies were more reliable. © 2024 IEEE.

关键词： Deep reinforcement learning

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Joint Trajectory Optimization and Mobile-Edge Computation Offloading for Multi-UAV-Connected System

Joint Trajectory Optimization and Mobile-Edge Computation Of...

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IEEE international conference on Communications (IEEE ICC)

作者： Li, Yang Ye, Liang Meng, WeiXiao Li, Cheng Harbin Inst Technol Commun Res Ctr Harbin Peoples R China Mem Univ Newfoundland Fac Engn & Appl Sci St John NF Canada

ISBN: (纸本)9781538674628

With the advance of various applications of unmanned aerial vehicles (UAVs), employing UAVs as mobile aerial base stations is a potential technology to improve wireless communication quality. However, due to limited load capacity, UAVs are hard to handle complex computing tasks. In this paper, a computation time minimization problem is investigated under a multi-UAV connected mobile edge computation (MEC) system, where both computation offloading and energy constrain are considered. The formulated problem is a mixed integer nonconvex optimization problem and is hard to solve. To simplify problem, we propose an efficient iterative algorithm by successive convex optimization techniques. Specific, we maximize the computation offloading under given tolerable delay. Bisection search is applied to find the optimal task completed time. For speed up algorithm convergence, a low-complexity association scheme is proposed based on greedy algorithm. Simulation result shows that the proposed algorithm can reduce time overhead and make full use of the gain brought by multiple UAVs.

关键词： unmanned aerial vehicle (UAV) mobile edge computing (MEC) multi-UAV minimize computing time trajectory optimization

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A Study on Resource Allocation for Mobile Edge computing-Assisted Industrial Iot

A Study on Resource Allocation for Mobile Edge Computing-Ass...

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2023 international conference on Networks, Communications and Intelligent computing, NCIC 2023

作者： Lu, Jie Zhang, Yuexia Li, Junjie Ministry of Information Industry Beijing Information Science and Technology University Key Laboratory of Information and Communication Systems Beijing China Control Technology Ministry of Education Beijing Information Science and Technology University Key Laboratory of Modern Measurement Beijing China

ISBN: (纸本)9798350371949

Since the advancement of mobile communication technology, traditional industries are gradually transforming and upgrading, which leads to problems such as insufficient computing resources and excessive energy consumption of Internet of Things (IoT) devices. To address this issue, a resource allocation algorithm for the mobile edge computing-assisted industrial IoT (IIoT) is proposed. First, a mobile edge computing-assisted IIoT network model was constructed. Using the collaboration between IoT devices, mobile edge computing (MEC) servers, and cloud servers, the issue of minimizing all IIoT devices energy cost was proposed. Subsequently, in order to address the optimization issue, obtain its optimal solution, the alternating direction method of multipliers (ADMM) algorithm is introduced. Finally, the ADMM algorithm's convergence was verified through an experimental simulation. In contrast to baseline algorithm 1 and algorithm 2, the ADMM algorithm can significantly diminish energy cost, proving the effectiveness of the proposed scheme. © 2023 IEEE.

关键词： Resource allocation

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Research on multi-task assignment method of multi-configuration UAV based on improved hybrid algorithm

Research on multi-task assignment method of multi-configurat...

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2024 international conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2024

作者： Zheng, Zitong Xia, Qingyuan Li, Shengwei Deng, Bohai Nanjing University of Science and Technology Nanjing China National Key Laboratory of Rotorcraft Aeromechanics Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (纸本)9781510685840

In this paper, an improved hybrid algorithm based on particle swarm optimization (PSO) and genetic algorithm (GA) is formed to solve the problem of "premature" and slow convergence. First of all, this paper improves the multi-task Collaborative assignment model (CMTAP), comprehensively considers the task completion, sequence, time, UAV type, quantity, energy consumption and other factors, and establishes a scientific and standardized assignment model that meets the task requirements. Secondly, for the problem of target allocation, this paper proposes an improved hybrid particle swarm optimization algorithm (PSO), which is based on particle swarm optimization (PSO), and uses genetic algorithm (GA) to increase the global search ability of the algorithm for the problem that the optimization of discrete nonlinear functions is very easy to fall into the local optimal solution. The mixed coding method of traditional coding and binary coding is used to solve the problem of uncertain number of unmanned aerial vehicles (UAVs). At the same time, the PSO algorithm is reduced by increasing the inertia weight, increasing the time-varying factor, and adopting the adaptive mutation probability, bit mutation and cross mutation methods for GA to increase the convergence speed and global search ability of the hybrid algorithm. Finally, the overall cost of the improved hybrid algorithm, hybrid algorithm and traditional algorithm is compared by simulation, and the effectiveness and accuracy of the proposed algorithm are verified. © 2024 SPIE.

关键词： Genetic algorithms

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Multi-modal emotion recognition in conversation based on speaker dependency

Multi-modal emotion recognition in conversation based on spe...

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2024 international conference on Cloud computing, Performance computing, and Deep Learning, CCPCDL 2024

作者： Zhang, Jiayi Yuan, Shaoyang Lu, Haiyan Cao, Juan State Key Laboratory of Media Convergence and Communication Communication University of China China Xinhua News Agency Communications Technology Bureau China

ISBN: (纸本)9781510683044

This paper, addressing the lack of interaction between context and modalities and the deficiency in speaker dependency in dialogue sentiment analysis, proposes a multimodal fusion method based on cross-modal attention. This method leverages cross-modal attention to facilitate pairwise interaction of textual, audio, and visual modality information. Additionally, it introduces a Speaker Dependency Encoder (SDE) network that divides historical dialogues and speaker features according to the identity of the speakers. This network divides historical conversations and speaker features according to speaker identity, and uses attention mechanism to explore inter-speaker and intra-speaker dependence from the divided conversations and speaker features respectively. The above dependencies are then spliced to obtain the speaker dependencies at the current moment. A multimodal dialogue sentiment analysis network, Speaker Guide Emotion Network (SGEN), is proposed, which combines the modal fusion network CMA and speaker feature generation network SDE proposed in this paper to undertake the task of multimodal dialogue sentiment analysis. © 2024 SPIE.

关键词： Emotion Recognition

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