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检索条件"任意字段=Conference on High Performance Computer Modernization Program"

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In-Cache Processing with Power-of-Two Quantization for Fast CNN Inference on CPUs

In-Cache Processing with Power-of-Two Quantization for Fast ...

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2023 International Technical conference on Circuits/Systems, computers, and Communications, ITC-CSCC 2023

作者： Woo, Joseph Lee, Seungtae Kim, Seongwook Byeon, Gwangeun Hong, Seokin Sungkyunkwan University Department of Electrical and Computer Engineering Korea Republic of Sungkyunkwan University Department of Ai System Engineering Korea Republic of

ISBN: (纸本)9798350326413

Convolutional Neural Networks (CNN) demand high computational capabilities, motivating researchers to leverage Processing-In-Memory (PIM) technology to achieve significant performance improvements. However, implementing complex arithmetic operations such as multiplication within memory is a significant challenge in the PIM architecture. To address this challenge, this paper proposes a PIM-enabled cache (PEC) architecture that utilizes shifters for performing multiplication operations at a low cost. We also introduce a filter-wise hardware-friendly Power-of-Two (POT) quantization scheme that quantizes weights into power-of-two values for specific filters to accelerate convolution operations with the PEC. Our experimental results demonstrate that the proposed PEC, together with the POT quantization, achieves 2.28x performance improvement on average with an accuracy degradation of 0.784%. © 2023 IEEE.

关键词： Memory architecture

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Lightweight Frequency Masker for Cross-Domain Few-Shot Semantic Segmentation 38

Lightweight Frequency Masker for Cross-Domain Few-Shot Seman...

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38th conference on Neural Information Processing Systems, NeurIPS 2024

作者： Tong, Jintao Zou, Yixiong Li, Yuhua Li, Ruixuan School of Computer Science and Technology Huazhong University of Science and Technology China

Cross-domain few-shot segmentation (CD-FSS) is proposed to first pre-train the model on a large-scale source-domain dataset, and then transfer the model to data-scarce target-domain datasets for pixel-level segmentation. The significant domain gap between the source and target datasets leads to a sharp decline in the performance of existing few-shot segmentation (FSS) methods in cross-domain scenarios. In this work, we discover an intriguing phenomenon: simply filtering different frequency components for target domains can lead to a significant performance improvement, sometimes even as high as 14% mIoU. Then, we delve into this phenomenon for an interpretation, and find such improvements stem from the reduced inter-channel correlation in feature maps, which benefits CD-FSS with enhanced robustness against domain gaps and larger activated regions for segmentation. Based on this, we propose a lightweight frequency masker, which further reduces channel correlations by an Amplitude-Phase Masker (APM) module and an Adaptive Channel Phase Attention (ACPA) module. Notably, APM introduces only 0.01% additional parameters but improves the average performance by over 10%, and ACPA imports only 2.5% parameters but further improves the performance by over 1.5%, which significantly surpasses the state-of-the-art CD-FSS methods. © 2024 Neural information processing systems foundation. All rights reserved.

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An Improved Modified Jaya Optimization Algorithm: Application to the Solution of Nonlinear Equation Systems 4th

An Improved Modified Jaya Optimization Algorithm: Applicatio...

引用

4th International conference on Numerical Computations: Theory and Algorithms, NUMTA 2023

作者： Silva, Bruno Guerreiro Lopes, Luiz Doctoral Program in Informatics Engineering University of Madeira Madeira Island Funchal Portugal Regional Secretariat for Education Science and Technology Regional Government of Madeira Funchal Portugal Faculty of Exact Sciences and Engineering University of Madeira Madeira Island Funchal9020-105 Portugal

ISBN: (纸本)9783031812408

Population-based metaheuristic algorithms have been used to solve challenging optimization problems, and numerous modifications to make these algorithms more efficient have been proposed. Jaya is one of these algorithms that has been modified and utilized to solve many hard optimization problems. One such modification to the original algorithm is the Modified Jaya (MJAYA) algorithm, which, like Jaya, uses information about the best and worst candidate solutions to determine the population’s search direction, thereby limiting and focusing the search. Recent research has demonstrated that the MJAYA algorithm is not effective at solving systems of nonlinear equations transformed into equivalent nonlinear optimization problems. Systems of nonlinear equations are probably the most difficult class of numerical mathematics problems to solve, which justifies the use of metaheuristic approaches in their resolution. This paper proposes a new population-based optimization algorithm, the Improved Modified Jaya (IM-Jaya) algorithm, which, despite being a generic metaheuristic optimization algorithm, is here used to solve systems of nonlinear equations. The proposed algorithm strikes a better balance between global exploration and local exploitation, allowing for a broader and more exhaustive exploration of the search space as well as a more efficient local search in its most promising regions. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimization algorithms

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RETHINKING MODEL ENSEMBLE IN TRANSFER-BASED ADVERSARIAL ATTACKS 12

RETHINKING MODEL ENSEMBLE IN TRANSFER-BASED ADVERSARIAL ATTA...

引用

12th International conference on Learning Representations, ICLR 2024

作者： Chen, Huanran Zhang, Yichi Dong, Yinpeng Yang, Xiao Su, Hang Zhu, Jun School of Computer Science Beijing Institute of Technology China Dept. of Comp. Sci. and Tech. Institute for AI Tsinghua-Bosch Joint ML Center THBI Lab BNRist Center Tsinghua University Beijing100084 China RealAI

It is widely recognized that deep learning models lack robustness to adversarial examples. An intriguing property of adversarial examples is that they can transfer across different models, which enables black-box attacks without any knowledge of the victim model. An effective strategy to improve the transferability is attacking an ensemble of models. However, previous works simply average the outputs of different models, lacking an in-depth analysis on how and why model ensemble methods can strongly improve the transferability. In this paper, we rethink the ensemble in adversarial attacks and define the common weakness of model ensemble with two properties: 1) the flatness of loss landscape;and 2) the closeness to the local optimum of each model. We empirically and theoretically show that both properties are strongly correlated with the transferability and propose a Common Weakness Attack (CWA) to generate more transferable adversarial examples by promoting these two properties. Experimental results on both image classification and object detection tasks validate the effectiveness of our approach to improving the adversarial transferability, especially when attacking adversarially trained models. We also successfully apply our method to attack a black-box large vision-language model - Google's Bard, showing the practical effectiveness. Code is available at https://***/huanranchen/AdversarialAttacks. © 2024 12th International conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Object detection

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Secure SVM Services with Multiple Service Providers and Subscribers in the Cloud 24

Secure SVM Services with Multiple Service Providers and Subs...

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24th IEEE International conference on high performance Computing and Communications, 8th IEEE International conference on Data Science and Systems, 20th IEEE International conference on Smart City and 8th IEEE International conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Lu, Ruiheng Wang, Xiaofen Ge, Mengyu Li, Jiasheng Yang, Haomiao School of Computer Science and Engineering University of Electronic Science and Technology of China ChengDu China

ISBN: (纸本)9798350319934

In recent years, cloud computing has emerged as a way to deliver high-quality and reliable services and the integration of cloud services offers many opportunities for broader business applications. Among them, cloud-based support vector machine services (SVMS) are widely used. However, the service suffers from model security and query privacy issues, and existing solutions are not suitable for practical SVMS with multiple service providers and multiple users. In this paper, we propose a secure SVM service (SSVMS) supporting multiple service providers and multiple users. Specifically, SSVMS has the following three key techniques: 1) we use vector homomorphic encryption (VHE) to ensure model security and query privacy;2) we design a pair of invertible matrices to support multiple service providers and multiple users;3) we construct a special matrix to hide multi-user queries to resist collusion attacks. The security analysis shows that SSVMS can guarantee model security and query privacy even for multi-service provider and multi-user scenarios. performance evaluation shows that SSVMS has high accuracy and low communication and computational cost, which guarantees the realistic effectiveness of SSVMS. © 2022 IEEE.

关键词： Cloud computing

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Improving Visual Speech Recognition for Small-Scale Datasets via Speaker Embedding 6

Improving Visual Speech Recognition for Small-Scale Datasets...

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6th International conference on Software Engineering and computer Science, CSECS 2023

作者： Wu, Jinghan Zhang, Yakun Zhang, Xingyu Zheng, Changyan Yan, Ye Yin, Erwei Academy of Medical Engineering and Translational Medicine Tianjin University Tianjin China Tianjin China Beijing China Intelligent Game and Decision Laboratory Beijing China High-tech Institute Weifang China

ISBN: (纸本)9798350306378

Visual speech recognition, also known as lipreading, breaks the application limitation of automatic speech recognition when there is extreme background noise or a need for private communication. However, acquiring high-quality lipreading models remains a challenging task due to the lack of sufficient lipreading data for particular languages. In this work, we propose a new lipreading framework using speaker identity information to improve the speech recognition performance with limited and low resolution visual data. Based on a shared visual encoder, speaker information is first pre-trained with speaker identification label. And then, it is further integrated with semantic features for the content recognition training process. The shared visual encoder adopts widely used visual front-end of deep convolutional neural network along with recurrent neural network, which can extract the spatial and temporal features effectively. Meanwhile, extra information is provided for the visual speech recognition since the effectiveness of supervised pre-training is utilized, alleviating the performance drop of the lipreading model caused by insufficient training data. Experimental results on the AVLetters dataset confirm the state-of-the-art recognition result of the proposed framework with the best classification accuracy of 70.77 %. For the simplicity and effectiveness, it is a promising lipreading framework for practical use in various application scenarios and languages. © 2023 IEEE.

关键词： Embeddings

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Cross-lingual Transfer for Automatic Question Generation by Learning Interrogative Structures in Target Languages

Cross-lingual Transfer for Automatic Question Generation by ...

引用

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

作者： Hwang, Seonjeong Kim, Yunsu Lee, Gary Geunbae Graduate School of Artificial Intelligence POSTECH Korea Republic of Computer Science and Engineering POSTECH Korea Republic of aiXplain Inc. Los GatosCA United States

ISBN: (纸本)9798891761643

Automatic question generation (QG) serves a wide range of purposes, such as augmenting question-answering (QA) corpora, enhancing chatbot systems, and developing educational materials. Despite its importance, most existing datasets predominantly focus on English, resulting in a considerable gap in data availability for other languages. Cross-lingual transfer for QG (XLT-QG) addresses this limitation by allowing models trained on high-resource language datasets to generate questions in low-resource languages. In this paper, we propose a simple and efficient XLT-QG method that operates without the need for monolingual, parallel, or labeled data in the target language, utilizing a small language model. Our model, trained solely on English QA datasets, learns interrogative structures from a limited set of question exemplars, which are then applied to generate questions in the target language. Experimental results show that our method outperforms several XLT-QG baselines and achieves performance comparable to GPT-3.5-turbo across different languages. Additionally, the synthetic data generated by our model proves beneficial for training multilingual QA models. With significantly fewer parameters than large language models and without requiring additional training for target languages, our approach offers an effective solution for QG and QA tasks across various languages. © 2024 Association for Computational Linguistics.

关键词： Question answering

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HR-UXNet: A high-Resolution Model for Brain Tumor Segmentation 3

HR-UXNet: A High-Resolution Model for Brain Tumor Segmentati...

引用

3rd International conference on computer Science, Electronic Information Engineering and Intelligent Control Technology, CEI 2023

作者： Zhu, Chunmao Lv, Yongqiang Song, Ge Ma, Hongxiao College of Electronics and Information Engineering Shandong University of Science and Technology Qingdao China The Neutron Times of Innovation Science & Technology Shandong Key Laboratory of Neutron Science and Technology Qingdao China

ISBN: (纸本)9798350372052

Semantic segmentation of brain tumors is a fundamental task in medical image analysis, involving multiple MRI modalities for diagnosing patients and studying malignant tumor progression. With the advancements in CNN and Transformer algorithms, recent 3D medical segmentation models, like VT-UNET, have achieved state-of-the-art performance in the BraTS brain tumor dataset. The majority of networks used for brain tumor semantic segmentation currently employ the U-Net architecture. While U-Net-like architectures offer rich contextual information, the process of recovering high-resolution representations from low-resolution ones may reduce the spatial accuracy of the resulting segmentation masks. In previous works, the design style of high-Resolution Network (HRNet) has been successfully applied to various 2D image segmentation tasks, such as human pose estimation, scene segmentation, and object segmentation. HRNet focuses on feature fusion between low-resolution and high-resolution representations, rather than aiming to recover high-resolution information from low-resolution ones. This approach is highly relevant for complex brain tumor segmentation tasks. Thus, in this paper, we introduce, for the first time, the utilization of the HRNet design style in the context of brain tumor segmentation. We employ the 3D UX-Net block to exchange information between feature maps of different resolutions. Extensive experiments demonstrate that on the BraTS 2021 dataset, the performance of HR-UXNet is comparable to advanced U-Net based models in brain tumor segmentation. © 2023 IEEE.

关键词： Semantic Segmentation

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Joint optimization of energy distribution, path selection and time allocation for smart reflector assisted mmWave communication system 9

Joint optimization of energy distribution, path selection an...

引用

9th International conference on Big Data Computing and Communications, BigCom 2023

作者： Yang, Enhui Xing, Yu Guo, Xing Anhui Jianzhu University Hefei China Anhui University School of Computer Science and Technology Hefei China

ISBN: (纸本)9798350331240

With the development of wireless communication in recent years, severe contradiction have emergenced between high speed communication requirement and spectrum resources shortage. The millimeter wave (mmWave) communication technology is proposed as a promising solution to such contradiction. However, mmWave also has its own shortcomings despite of excellent communication performance, such as the line of sight (LOS) signal might easily being blocked by various obstacles. In order to relief the issue, this paper proposes a mmWave communication scheme with help of multiple co-existing smart reflectors. In order to coordinate the reflectors for better communication performance, we investigate the joint optimization problem involving energy distribution at transmitter (TX) side, reflection direction adjustment at reflector (RE) side, and time splitting among different receivers (RXs). We decouple the problem into three sub-problems, i.e., the low-layer energy distribution sub-problem solved with Lagrangian multiplier method, the mid-layer path selection sub-problem addressed with greedy-based algorithm, and high-layer time-allocation sub-problem managed through dynamic-programming method. We further investigate the context sensing tasks, and propose hash-based fast beam alignment algorithm with log time complexity, along with a least squares channel estimation algorithm. Extensive experiments are conducted, and the results demonstrate the effectiveness of the proposed solution. © 2023 IEEE.

关键词： Millimeter waves

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A Method of Knowledge Graph Construction for the Field of Mechanical Manufacturing Processes

A Method of Knowledge Graph Construction for the Field of Me...

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2023 International conference on Algorithms, high performance Computing, and Artificial Intelligence, AHPCAI 2023

作者： Zhou, Nan Wang, Yajun Jin, Bo School of Computer Science and Technology Dalian University of Technology Liaoning China School of Mechanical Engineering Dalian University of Technology Liaoning Dalian China School of Innovation and Entrepreneurship Dalian University of Technology Liaoning China

ISBN: (纸本)9781510671881

Mechanical manufacturing process knowledge exhibits complexity, detail-oriented nature, experiential characteristics, high learning cost, and continuous evolution, posing challenges in its management, utilization, and transmission. Enterprises currently face low knowledge reuse rates and high knowledge management costs. To address these issues, we propose a novel approach utilizing deep learning models to extract entities and relationships from unstructured knowledge, constructing a process knowledge graph. With deep learning's powerful comprehension abilities, we automatically extract critical information, organizing it into a structured knowledge graph consisting of 12 entities and 12 relationships. The process knowledge graph enables convenient knowledge transmission and sharing, increasing reuse rates and reducing redundant learning. It serves as an effective tool for process formulation, enhancing efficiency and enterprise benefits. Additionally, it facilitates prompt identification of suitable process parameters, accurate prediction of manufacturing issues, and timely adjustments. Moreover, the process knowledge graph fosters innovation by identifying optimization solutions and improving product quality and cost reduction. This approach offers significant competitive advantages and commercial value to the manufacturing industry. © 2023 SPIE. All rights reserved.

关键词： Knowledge management

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