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REFROM: Responsive, Energy-Efficient Frame Rendering for Mobile Devices

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REFROM: Responsive, Energy-Efficient Frame Rendering for Mob...

International Symposium on Low Power Electronics and Design (ISLPED)

作者： Tsung-Yen Hsu Yi-Shen Chen Yun-Chih Chen Yuan-Hao Chang Tei-Wei Kuo Department of Computer Science and Information Engineering National Taiwan University Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Institute of Information Science Academia Sinica Mohamed bin Zayed University of Artificial Intelligence High Performance and Scientific Computing Center National Taiwan University

The increasing demand for high-quality graphics on mobile devices necessitates a high frame rate for display refresh. However, current process scheduling and memory management policies fail to consider the computation demands of frame rendering because they are optimized for saving energy and resource utilization. This leads to unresponsive displays for mobile users due to rendering delays. Accurately estimating computation demands is challenging for the mobile operating system, particularly under memory pressure, without display-specific semantics from user space. Moreover, the complexity of frame rendering makes it infeasible to schedule them with real-time policies. To address these issues, we propose a new framework called REFROM that utilizes a history-based frame time estimator to analyze frame time samples from UI threads and predict the computation requirements of upcoming frames. Experimental results demonstrate that REFROM reduces the number of delayed frames by up to 40% and improves up to 4% energy efficiency compared to the existing approaches.

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Game Solving with Online Fine-Tuning

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arXiv 2023年

作者： Wu, Ti-Rong Guei, Hung Wei, Ting Han Shih, Chung-Chin Chin, Jui-Te Wu, I-Chen Institute of Information Science Academia Sinica Taiwan Department of Computing Science University of Alberta Canada Department of Computer Science National Yang Ming Chiao Tung University Taiwan Research Center for Information Technology Innovation Academia Sinica Taiwan

Game solving is a similar, yet more difficult task than mastering a game. Solving a game typically means to find the game-theoretic value (outcome given optimal play), and optionally a full strategy to follow in order to achieve that outcome. The AlphaZero algorithm has demonstrated super-human level play, and its powerful policy and value predictions have also served as heuristics in game solving. However, to solve a game and obtain a full strategy, a winning response must be found for all possible moves by the losing player. This includes very poor lines of play from the losing side, for which the AlphaZero self-play process will not encounter. AlphaZero-based heuristics can be highly inaccurate when evaluating these out-of-distribution positions, which occur throughout the entire search. To address this issue, this paper investigates applying online fine-tuning while searching and proposes two methods to learn tailor-designed heuristics for game solving. Our experiments show that using online fine-tuning can solve a series of challenging 7x7 Killall-Go problems, using only 23.54% of computation time compared to the baseline without online fine-tuning. Results suggest that the savings scale with problem size. Our method can further be extended to any tree search algorithm for problem solving. Our code is available at https://***/papers/neurips2023-online-fine-tuning-solver. Copyright © 2023, The Authors. All rights reserved.

关键词： Trees (mathematics)

QNN-QRL: Quantum Neural Network Integrated with Quantum Reinforcement Learning for Quantum Key Distribution

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arXiv 2025年

作者： Behera, Bikash K. Al-Kuwari, Saif Farouk, Ahmed Pvt. Ltd. WB Mohanpur741246 India Qatar Center for Quantum Computing College of Science and Engineering Hamad Bin Khalifa University Doha Qatar Department of Computer Science Faculty of Computers and Artificial Intelligence Hurghada University Hurghada Egypt

Quantum key distribution (QKD) has emerged as a critical component of secure communication in the quantum era, ensuring information-theoretic security. Despite its potential, there are issues in optimizing key generation rates, enhancing security, and incorporating QKD into practical implementations. This research introduces a unique framework for incorporating quantum machine learning (QML) algorithms, notably quantum reinforcement learning (QRL) and quantum neural networks (QNN), into QKD protocols to improve key generation performance. Here, we present two novel QRL-based algorithms, QRL-V.1 and QRL-V.2, and propose the standard BB84 and B92 protocols by integrating QNN algorithms to form QNN-BB84 and QNN-B92. Furthermore, we combine QNN with the above QRL-based algorithms to produce QNN-QRL-V.1 and QNN-QRL-V.2. These unique algorithms and established protocols are compared using evaluation metrics such as accuracy, precision, recall, F1 score, confusion matrices, and ROC curves. The results from the QNN-based proposed algorithms show considerable improvements in key generation quality. The existing and proposed models are investigated in the presence of different noisy channels to check their robustness. The proposed integration of QML algorithms into QKD protocols and their noisy analysis create a new paradigm for efficient key generation, which advances the practical implementation of QKD systems. © 2025, CC BY.

关键词： Quantum cryptography

Automatic Section Classification in Spanish Clinical Narratives Using Chunked Named Entity Recognition

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Automatic Section Classification in Spanish Clinical Narrati...

2023 Iberian Languages Evaluation Forum, IberLEF 2023

作者： Carvallo, Andrés Rojas, Matías Muñoz-Castro, Carlos Aracena, Claudio Guerra, Rodrigo Pizarro, Benjamín Dunstan, Jocelyn National Center for Artificial Intelligence Santiago Chile Faculty of Physical and Mathematical Sciences Universidad de Chile Santiago Chile Department of Computer Science Pontificia Universidad Católica de Chile Santiago Chile Institute for Mathematical Computing Pontificia Universidad Católica de Chile Santiago Chile Center for Mathematical Modeling Santiago Chile Millenium Institute Foundational Research on Data Santiago Chile

The extraction and classification of important information from Spanish Electronic Clinical Narratives (ECNs) can be challenging due to the complexity of the clinical text and the limited availability of labeled data. In this paper, we introduce a chunked Named Entity Recognition model designed to parse and classify sections of ECNs into predefined categories. The model aims to improve section identification and classification accuracy within ECNs in the context of the IberLEF ClinAIS Task. Our system achieves a promising performance, obtaining a weighted B2 score of.6958, demonstrating its capability to accurately distinguish borders and boundaries between sections. The paper concludes with a comprehensive analysis of the results, discussing potential implications and suggesting directions for further improvements in clinical text analysis. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Classification (of information)

Removal of degradation from ancient stone inscription images using deep learning techniques

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Removal of degradation from ancient stone inscription images...

3rd International Conference on Machine Learning and Data Engineering, ICMLDE 2024

作者： Nair, Bipin B.J. Veiyo, P.M. Gupta, Deepti Ravi, Vinayakumar Vijay, G. Sachin, A.S. Akhil, S. Department of Computer Science School of Computing Amrita Vishwa Vidyapeetham Mysore India Subhani Department of Computer Information Systems Texas A&M University-Central Texas KilleenTX76549 United States Center for Artificial Intelligence Prince Mohammad Bin Fahd University Khobar Saudi Arabia

Important knowledge, wisdom, and language are lost over time due to fading, erosion, and environmental variables that complicate ancient stone inscriptions. To help with protection efforts, the proposed study employs an image binarization model based on well-known deep neural networks algorithms. The methods durability is enhanced, and a more effective stone engraving preservation strategy is ensured by the application of multiple deep neural networks architectures. This research presents a model that has attained an incredible average 90.76% accuracy. This was made possible by extensive training with 437 images from temples in Karnataka. The deep neural networks technique is used to enhance the efficiency and accuracy of stone inscription preservation while reducing the challenges caused by environmental factors such as algae. © 2025 The Author(s).

关键词： Deep neural networks

Towards Cost-Optimal Policies for DAGs to Utilize IaaS Clouds with Online Learning

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IEEE Transactions on Services computing 2025年

作者： Wu, Xiaohu Yu, Han Casale, Giuliano Gao, Guanyu Beijing University of Posts and Telecommunications National Engineering Research Center of Mobile Network Technologies Beijing China Nanyang Technological University WeBank-NTU Joint Research Centre on Fintech Singapore Singapore Nanyang Technological University School of Computer Science and Engineering Singapore Singapore Imperial College London Department of Computing London United Kingdom Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China

Premier cloud service providers (CSPs) offer on-demand and spot instances (i.e., virtual machines) with time-varying features in availability and price. While interacting with a CSP, what concerns users is the process of cost-effectively utilizing these instances, possibly in addition to self-owned instances. A job in data-intensive applications is represented by a directed acyclic graph (DAG) whose nodes represent tasks and the DAG can further be transformed into a chain of tasks. The key to achieving cost efficiency is determining the allocation of a specific deadline to each task, as well as the allocation of different types of instances to the task. In this paper, we make some mild assumptions on the usage of various instances and give an analytical understanding of the expected behaviors while utilizing instances. Based on this, we propose informed heuristic policies to determine the allocation of deadlines and instances. The policies are parametric to support the usage of online learning to infer the optimal values against the dynamics of cloud markets. Finally, intuitive greedy policies are used as baselines to validate the effectiveness of the proposed analytical solutions. The cost improvement is up to 23.58% when spot and on-demand instances are considered and up to 50.08% when self-owned instances are also considered. © 2008-2012 IEEE.

关键词： Cloud platforms

Slimmable transformer with hybrid axial-attention for medical image segmentation

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computers in Biology and Medicine 2024年 173卷 108370-108370页

作者： Hu, Yiyue Mu, Nan Liu, Lei Zhang, Lei Jiang, Jingfeng Li, Xiaoning College of Computer Science Sichuan Normal University Chengdu610101 China School of Science and Engineering The Chinese University of Hong Kong Shenzhen Shenzhen518172 China Department of Biomedical Engineering Michigan Technological University HoughtonMI49931 United States Visual Computing and Virtual Reality Key Laboratory of Sichuan Sichuan Normal University Chengdu610068 China Education Big Data Collaborative Innovation Center of Sichuan 2011 Chengdu610101 China

The transformer architecture has achieved remarkable success in medical image analysis owing to its powerful capability for capturing long-range dependencies. However, due to the lack of intrinsic inductive bias in modeling visual structural information, the transformer generally requires a large-scale pre-training schedule, limiting the clinical applications over expensive small-scale medical data. To this end, we propose a slimmable transformer to explore intrinsic inductive bias via position information for medical image segmentation. Specifically, we empirically investigate how different position encoding strategies affect the prediction quality of the region of interest (ROI) and observe that ROIs are sensitive to different position encoding strategies. Motivated by this, we present a novel Hybrid Axial-Attention (HAA) that can be equipped with pixel-level spatial structure and relative position information as inductive bias. Moreover, we introduce a gating mechanism to achieve efficient feature selection and further improve the representation quality over small-scale datasets. Experiments on LGG and COVID-19 datasets prove the superiority of our method over the baseline and previous works. Internal workflow visualization with interpretability is conducted to validate our success better;the proposed slimmable transformer has the potential to be further developed into a visual software tool for improving computer-aided lesion diagnosis and treatment planning. © 2024 Elsevier Ltd

关键词： COVID-19

Optimizing Communication for Latency Sensitive HPC Applications on up to 48 FPGAs Using ACCL

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arXiv 2024年

作者： Meyer, Marius Kenter, Tobias Petrica, Lucian O’Brien, Kenneth Blott, Michaela Plessl, Christian Paderborn University Department of Computer Science Warburger Str. 100 Paderborn Germany Paderborn Center for Parallel Computing Mersinweg 5 Paderborn Germany AMD Research 2020 Bianconi Ave Dublin Ireland

Most FPGA boards in the HPC domain are well-suited for parallel scaling because of the direct integration of versatile and high-throughput network ports. However, the utilization of their network capabilities is often challenging and error-prone because the whole network stack and communication patterns have to be implemented and managed on the FPGAs. Also, this approach conceptually involves a trade-off between the performance potential of improved communication and the impact of resource consumption for communication infrastructure, since the utilized resources on the FPGAs could otherwise be used for computations. In this work, we investigate this trade-off, firstly, by using synthetic benchmarks to evaluate the different configuration options of the communication framework ACCL and their impact on communication latency and throughput. Finally, we use our findings to implement a shallow water simulation whose scalability heavily depends on low-latency communication. With a suitable configuration of ACCL, good scaling behavior can be shown to all 48 FPGAs installed in the system. Overall, the results show that the availability of inter-FPGA communication frameworks as well as the configurability of framework and network stack are crucial to achieve the best application performance with low latency communication. Copyright © 2024, The Authors. All rights reserved.

关键词： Field programmable gate arrays (FPGA)

The curse of random quantum data

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arXiv 2024年

作者： Zhang, Kaining Liu, Junyu Liu, Liu Jiang, Liang Hsieh, Min-Hsiu Tao, Dacheng School of Computer Science Faculty of Engineering University of Sydney Australia Pritzker School of Molecular Engineering The University of Chicago ChicagoIL60637 United States Department of Computer Science The University of Chicago ChicagoIL60637 United States Kadanoff Center for Theoretical Physics The University of Chicago ChicagoIL60637 United States Department of Computer Science The University of Pittsburgh PittsburghPA15260 United States Hon Hai Quantum Computing Research Center Taipei Taiwan

Quantum machine learning, which involves running machine learning algorithms on quantum devices, may be one of the most significant flagship applications for these devices. Unlike its classical counterparts, the role of data in quantum machine learning has not been fully understood. In this work, we quantify the performances of quantum machine learning in the landscape of quantum data. Provided that the encoding of quantum data is sufficiently random, the performance, we find that the training efficiency and generalization capabilities in quantum machine learning will be exponentially suppressed with the increase in the number of qubits, which we call "the curse of random quantum data". Our findings apply to both the quantum kernel method and the large-width limit of quantum neural networks. Conversely, we highlight that through meticulous design of quantum datasets, it is possible to avoid these curses, thereby achieving efficient convergence and robust generalization. Our conclusions are corroborated by extensive numerical simulations. © 2024, CC BY.

关键词： Quantum computers