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Execution Management of distributed Quantum computing Jobs

Execution Management of Distributed Quantum Computing Jobs

引用

Quantum computing and Engineering (QCE), IEEE international conference on

作者： Davide Ferrari Michele Bandini Michele Amoretti Department of Engineering and Architecture Quantum Software Laboratory University of Parma Parma Italy

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

In a distributed quantum computation, a large quantum circuit gets sliced into sub-circuits that must be executed at the same time on a quantum computing cluster. The interactions between the sub-circuits are usually defined in terms of non-local gates that require shared entangled pairs and classical communication between different nodes. Assuming that multiple end users submit distributed quantum computing (DQC) jobs to the cluster, an execution management problem arises. This is actually a parallel job scheduling problem, in which a set of jobs of varying processing times need to be scheduled on multiple machines while trying to minimize the length of the schedule. In a previous work, we started investigating the problem considering random circuits and approximating the length of each DQC job with the number of layers of the circuit. In this work, we put forward the study by considering a more realistic model for estimating DQC job lengths and by performing evaluations with circuits of practical interest.

关键词： Measurement Schedules Processor scheduling Quantum entanglement Computational modeling Logic gates Scheduling Quantum networks Quantum circuit Integrated circuit modeling

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Energy Efficient Priority-Based Task Scheduling for Computation Offloading in Fog computing 1

引用

21st international conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Yin, Jiaying Fu, Jing Wu, Jingjin Zheng, Shiming BNU HKBU United Int Coll Div Sci & Technol Zhuhai Guangdong Peoples R China RMIT Univ Sch Engn Melbourne Vic Australia

ISBN: (数字)9783030953843

ISBN: (纸本)9783030953843;9783030953836

Fog computing offers a flexible solution for computational offloading for Internet of Things (IoT) services at the edge of wireless networks. It serves as a complement to traditional cloud computing, which is not cost-efficient for most offloaded tasks in IoT applications involving small-to-medium levels of computing tasks. Given the heterogeneity of tasks and resources in fog computing, it is vital to offload each task to an appropriate destination to fully utilize the potential benefit of this promising technology. In this paper, we propose a scalable priority-based index policy, referred to as the Prioritized Incremental Energy Rate (PIER), to optimize the energy efficiency of the network. We demonstrate that PIER is asymptotically optimal in a special case applicable for local areas with high volumes of homogeneous offloaded tasks and exponentially distributed task durations. In more general cases with statistically different offloaded tasks, we further demonstrate the improvement of PIER over benchmark policies in terms of energy efficiency and the robustness of PIER to different task duration distributions by extensive simulations. Our results show that PIER can perform better than benchmark policies in more than 78.6% of all simulation runs.

关键词： Priority-based policies Energy efficiency Fog computing Computational offloading Internet of Things

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A Deep Learning Approach for Human Face Sentiment Classification 21

A Deep Learning Approach for Human Face Sentiment Classifica...

引用

21st ACIS international Winter conference on Software Engineering, Artificial Intelligence, Networking and parallel/distributed computing (SNPD-Winter)

作者： Singh, Chetanpal Wibowo, Santoso Grandhi, Srimannarayana CQUniversity Sch Engn & Technol Melbourne Vic Australia

ISBN: (纸本)9781665418430

This paper presents the development of a deep learning approach for human face sentiment classification. Bidirectional Long-Short Term Memory (Bi-LSTM) and recurrent neural networks (RNNs) are used for object-based segmentation in images, and CNN-RNN model is adopted for nonlinear mapping. To test the applicability of this proposed approach, we have trained several deep neural networks to recognize facial expressions in 10,000 images. Our experiments show that the proposed approach can achieve an accuracy of 99.12% in classifying human face sentiment. Moreover, results indicate that the model not only boosts the classification model but also lessen the overhead.

关键词： Image classification deep learning convolution neural network big data recurrent neural networks

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Quantum Convolutional Neural Network based on Variational Quantum Fuzzy Inference

Quantum Convolutional Neural Network based on Variational Qu...

引用

Information Science, parallel and distributed Systems (ISPDS), international conference on

作者： FangCong Zheng ZhaoLin Liu ShiBin Zhang School of Cyberspace Security Chengdu University of Information Technology Chengdu China

ISBN: (数字)9798350352719

ISBN: (纸本)9798350352726

In recent years, with the rapid development of quantum technology, quantum machine learning has attracted wide attention. In this paper, we propose a novel quantum machine learning model which introduces variational quantum gates in the quantum fuzzy inference circuits, and adjusts the variational gate parameters to automatically learn fuzzy rules for data classification through quantum machine learning training. Comparison results on the iris dataset show that our variational quantum fuzzy inference quantum convolutional neural network (VQFI-QCNN) outperforms classical multi layer perceptron (MLP) neural networks with a similar number of parameters, demonstrating the potential advantages of quantum. Due to the better results on the iris structured dataset, our VQFI-QCNN deserves further investigation, and as the number of quantum bits available for quantum computers increases, our method may also be applied in the future to efficiently classify structured data in mega data.

关键词： Fuzzy logic Training Iris Quantum computing Neural networks distributed databases Machine learning

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MLPs: Efficient Training of MiniGo on Large-scale Heterogeneous computing System

MLPs: Efficient Training of MiniGo on Large-scale Heterogene...

引用

international conference on parallel and distributed Systems (ICPADS)

作者： Peng Qiao Zhouyu He Rongchun Li Jingfei Jiang Yong Dou Dongsheng Li Science and Technology on Parallel and Distributed Laboratory National University of Defense Technology Changsha China

Deep Reinforcement Learning has been successfully applied in various applications and achieved impressive performance compared with previous traditional methods but suffers from high computation cost and long training time. MLPerf takes deep reinforcement learning as one of the benchmark tracks and provides a single node training version of MiniGo as a reference. A key challenge is to achieve efficient MiniGo training on a large-scale computing system. According to the training computation pattern in MiniGo and the characteristics of our large-scale heterogeneous computing system, we propose a MultiLevel parallel strategy, MLPs, including task-level parallelism between nodes, CPU-DSP heterogeneous parallelism, and DSP multi-core parallelism. The proposed method reduces the overall execution time from 43 hours to 16 hours while scaling the node size from 1067 to 4139. The scaling efficiency is 69.1%. According to our fitting method, the scaling efficiency is 46.5% when scaling to 8235 nodes. The experimental results show that the proposed method achieves the efficient training of MiniGo on the largescale heterogeneous computing system.

关键词： Training Deep learning Neural networks Memory management Reinforcement learning parallel processing Programming

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parallel Dual-Gate Thin-Film Transistors for Sensing and Neuromorphic computing 16

Parallel Dual-Gate Thin-Film Transistors for Sensing and Neu...

引用

16th IEEE international conference on Solid-State and Integrated Circuit Technology, ICSICT 2022

作者： Hu, Yushen Lei, Tengteng Wong, Man The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong The Hong Kong University of Science and Technology Shenzhen Research Institute Shenzhen China

ISBN: (数字)9781665469067

ISBN: (纸本)9781665469067

Of the two gate electrodes sandwiching the channel region of a parallel, dual-gate (DG) thin-film transistor (TFT), one can be used for modulating the threshold voltage associated with the other. This unique capability is used in sensing to set the operating point of the sensing electrode and in neuromorphic computing to adjust the "weight"of the input. Made of units each containing a DG TFT with an extended top sensing gate, a tactile sensor array is demonstrated for distributed force sensing. Capable of emulating both the excitative and inhibitive transmission of a signal across a synaptic gap, a unit containing such a DG TFT combined with a memory capacitor is replicated and organized to construct an artificial neural network (ANN). Lastly, the monolithic merging of a sensing array and an ANN is proposed. © 2022 IEEE.

关键词： Neural networks

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WideVision: A Low-Power, Multi-Protocol Wireless Vision Platform for distributed Surveillance 18

WideVision: A Low-Power, Multi-Protocol Wireless Vision Plat...

引用

18th international conference on Wireless and Mobile computing, Networking and Communications (WiMob)

作者： Scherer, Moritz Sidler, Fabian Rogenmoser, Michael Magno, Michele Benini, Luca Swiss Fed Inst Technol Dept Informat Technol & Elect Engn Zurich Switzerland Univ Bologna Dept Elect Elect & Informat Engn Bologna Italy

ISBN: (数字)9781665469753

ISBN: (纸本)9781665469753

The trend in Internet of Things research points toward performing increasingly compute-intensive data analysis tasks on embedded sensor nodes, rather than server centers. Exploiting the technological advances in both energy efficiency, and Tiny Machine Learning algorithms and methods, an increasing number of recognition and classification tasks can be performed by small, low-power, wireless sensor nodes. This paper presents WideVision, a wireless, wide-area sensing platform capable of performing on-board person detection with power requirements in the mW range. The WideVision platform integrates seamlessly into the Internet of Things, by coupling a dedicated multiradio platform, including a LoRa interface, enabling medium and long-range communication, with a novel parallel RISC-V microcontroller. We evaluate the proposed platform with the GAP8 microcontroller, which includes an 8-core RISC-V cluster, and greyscale camera to perform person detection by training and deploying an advanced, quantized neural network, achieving a statistical accuracy 84.5% for a 5-person detection task with a latency of only 182 ms. Experimental results demonstrate that the WideVision sensor node platform while performing inference at a rate of one image per minute on-board, is capable of lasting 300 days on a 2400 mAh Li-ion battery, and 65 days when evaluating one image per 10 seconds while providing effective surveillance of its perimeter.

关键词： Wireless communication Training Wireless sensor networks Power measurement Microcontrollers Surveillance Battery charge measurement

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parallel Physics-Informed Neural networks & Giza Pyramid Construction Optimization Algorithm for FDIAs Detection in Industrial IoT:ML Based Techniques

Parallel Physics-Informed Neural Networks & Giza Pyramid Con...

引用

Sustainable computing and Smart Systems (ICSCSS), international conference on

作者： Basi Reddy A R. Yogesh Rajkumar Dept. of CSE Bharath institute of higher education and research Chennai Tamilnadu India Dept. of IT Bharath institute of higher education and research Chennai Tamilnadu India

ISBN: (数字)9798350379990

ISBN: (纸本)9798350391558

The rapid evolution of IIoT (Industrial Internet of Things) in computing has brought about numerous security concerns, among which is the looming threat of False Data Injection (FDI) attacks. To address these attacks, a study introduces a novel approach called MLBT-FDIA-IIoT (Fault Data Injection Attack Detection in IIoT using parallel Physics-Informed Neural networks with Giza Pyramid Construction Optimization algorithm). This method makes use of real-time sensor data for attack detection. The data is preprocessed using distributed Set-Membership Fusion Filtering (DSMFF) to remove noise. Then, it is fed into a neural network for classification. Specifically, parallel Physics-Informed Neural networks (PPINN) are used to distinguish between normal operations and False Data Injection Attacks (FDIAs). However, PPINN lacks optimization methods for accurate detection. To address this, the study proposes the Giza Pyramid Construction Optimization algorithm (GPCOA). This algorithm optimizes the PPINN classifier to detect attacks with more precision. The proposed MLBT-FDIA-IIoT method is implemented using MATLAB and evaluates various metrics such as accuracy, recall, and precision. The results demonstrate significant improvements compared to existing techniques such as MLT-FDI-IIoT, FDIA-FDAS-IIoT, and DCDD-IIoT-FDIA.

关键词： Training Deep learning Support vector machines Correlation Accuracy Neural networks Data models

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A Novel Multi-objective Neural Architecture Search Algorithm via Gaussian Progress Sampling 5

A Novel Multi-objective Neural Architecture Search Algorithm...

引用

5th IEEE international conference on Artificial Intelligence and Big Data, ICAIBD 2022

作者： Chen, Xuehui Jiang, Jingfei Niu, Xin Pan, Hengyue Dong, Peijie Wei, Zimian National University of Defense Technology National Laboratory for Parallel and Distributed Processing Changsha China

ISBN: (纸本)9781665499132

Multi-objective neural architecture search (NAS) algorithms aim to automatically search the neural architecture suitable for different computing power platforms by using multi-objective optimization methods. The LEMONADE algorithm, which is a representative algorithm of multi-objective NAS algorithms, maintains a population of networks on an approximation of the Pareto front of the multiple objectives, such as predictive performance, number of parameters or FLOPs. To address the irrationality and repeatability of only sampling based on cheap objectives in LEMONADE, we propose a novel multi-objective neural architecture search algorithm via Gaussian Process sampling, dubbed GP-LEMONADE. Meanwhile, to make the sampling process more efficient, we design the online predictor based on Gaussian Process to predict expensive objectives, and sample candidate networks by combining cheap objectives and expensive objectives, so as to ensure the rationality and efficiency of sampling. Experiments show that the GP-LEMONADE algorithm evolves 100 generations and obtains the SOTA model with 3.98% test error. This process only takes 7.38 GPU days, which is 26.75 GPU days shorter than that of LEMONADE. Our methods have improved the performance of the LEMONADE algorithm and ensured the rationality and efficiency of sampling during the evolution, which effectively improving the search efficiency of multi-objective NAS algorithms. © 2022 IEEE.

关键词： Multiobjective optimization

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parallel/distributed Intelligent Hyperparameters Search for Generative Artificial Neural networks

Parallel/Distributed Intelligent Hyperparameters Search for ...

引用

35th international ISC High Performance conference

作者： Esteban, Mathias Toutouh, Jamal Nesmachnow, Sergio Univ Republica Montevideo Uruguay Univ Malaga Malaga Spain MIT Cambridge MA 02139 USA

ISBN: (纸本)9783030905392;9783030905385

This article presents a parallel/distributed methodology for the intelligent search of the hyperparameters configuration for generative artificial neural networks (GANs). Finding the configuration that best fits a GAN for a specific problem is challenging because GANs simultaneously train two deep neural networks. Thus, in general, GANs have more configuration parameters than other deep learning methods. The proposed system applies the iterated racing approach taking advantage of parallel/distributed computing for the efficient use of resources for configuration. The main results of the experimental evaluation performed on the MNIST dataset showed that the parallel system is able to efficiently use the GPU, achieving a high level of parallelism and reducing the computational wall clock time by 78%, while providing competitive comparable results to the sequential hyperparameters search.

关键词： Hyperparameters search Artificial neural networks Generative adversarial networks Iterated race

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