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检索条件"任意字段=7th International Conference on Algorithms and Architectures for Parallel Processing"

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DIAG: A Refined Four-layer Agile Hardware Developing Flow for Generating Flexible Reconfigurable architectures 25

DIAG: A Refined Four-layer Agile Hardware Developing Flow fo...

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30th Asia and South Pacific Design Automation conference, ASP-DAC 2025

作者： Hui, Haojia Gu, Jiangyuan Hu, Xunbo Wei, Shaojun Yin, Shouyi School of Integrated Circuits Tsinghua Univ. Beijing China Integrated Circuit Research Platform International Innovation Center of Tsinghua Univ. Shanghai China

ISBN: (纸本)9798400706356

Rapid evolution in application algorithms, exemplified by advancements in artificial intelligence, wireless communication, and sciencific computing, necessitates a focus on developing energy-efficient, highly-flexible parallel computing architectures. this urgency is further amplified by the need for agile hardware development techniques to mitigate design complexity and reduce costs. Among emerging agile hardware development techniques, generative HDL stands out due to its straightforward grammatical structure and compatibility with hardware design thinking, yet it remains underutilized. In response, this paper introduces a novel four-layer agile developing flow, termed DIAG, innovatively leveraging unique Plugin-Service technology. the DIAG framework is applied to an extensible reconfigurable architecture generator, enabling the generation of diverse CGRA designs suitable for accelerating task computations across multiple application domains. Our comprehensive experiments on the CGRA generator design validate the efficiency of the DIAG flow and underscore generative-HDL's significant potential for complex, large-scale hardware development. © 2025 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Reconfigurable architectures

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Hybridization of One- and Two-Point Bandits Convex Optimization in Non-stationary Environments 24th

Hybridization of One- and Two-Point Bandits Convex Optimiz...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Zeng, Gailun Guo, Jianxiong Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China Hong Kong Baptist University Hong Kong Guangdong Key Lab of AI and Multi-modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9789819615278

Bandit Convex Optimization (BCO) is an imperative analysis framework when dealing with sequential decision-making problems. Considering to balance the computational cost and bounds of regrets, in this paper, we propose a hybridized algorithm of one- and two-point bandit convex models in non-stationary environments and use a more general performance measure dynamic regret, which records the cumulative difference between function loss and a feasible comparator sequence during the time horizon T. the path length of a comparator sequence PT reveals the non-stationarity of environments. Our proposed algorithm builds an upper bound of dynamic regret O((1+PT)1/2[β(λT)1/2+((1-λ)T)3/4]), where the parameter λ can dynamically adjust the bound guarantee to balance the computational cost in real applications. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Comparators (optical)

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parallel Implementation of Sieving Algorithm on Heterogeneous CPU-GPU Computing architectures 19th

Parallel Implementation of Sieving Algorithm on Heterogeneou...

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19th international conference on Information Security Practice and Experience

作者： Wu, Mengsi Li, Pei Chen, Jiageng Yao, Shixiong Cent China Normal Univ Wuhan 430079 Peoples R China

ISBN: (纸本)9789819790524;9789819790531

Sieving algorithms currently represent the fastest approach of solving the Shortest Vector Problem (SVP). However, current sieving algorithms exclusively utilize either CPUs or GPUs. this paper introduces a novel sieving approach tailored to CPU+GPU heterogeneous computing platforms. We constructed a runtime system capable of concurrently executing both CPU and GPU versions of the sieving algorithm. the GPU version of the sieving algorithm reduces the demand for graphics memory by efficiently transferring data in batches. We used two computing platforms to evaluate our method: Hannibal is equipped with an AMD Ryzen 7 5800H CPU and an NVIDIA RTX 3060 GPU;Zeus is equipped with an Intel Xeon Platinum 8176M CPU and two Integrated Matrox G200eW3 Graphics Controllers. the experimental results show that, compared to the classical sieving algorithm implementation in G6K, the proposed method achieves a minimum speedup of 7.2x (for 30-dimensional SVP) and a maximum of 588x (for 120-dimensional SVP) on Hannibal and a minimum speedup of 4.3x (for 30-dimensional SVP) and a maximum of 1230x (for 120-dimensional SVP) on Zeus.

关键词： SVP Sieving Heterogeneous Computing CUDA

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Dynamic Offloading Control for Waste Sorting Based on Deep Q-Network 24th

Dynamic Offloading Control for Waste Sorting Based on Deep...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Wang, Jing Wang, Xiaoyang Guo, Jianxiong Tang, Zhiqing Ding, Xingjian Wang, Tian Advanced Institute of Natural Sciences Beijing Normal University Zhuhai China Guangdong Key Lab of AI and Multi-modal Data Processing Department of Computer Science BNU-HKBU United International College Zhuhai China Faculty of Information Technology Beijing University of Technology Beijing China

ISBN: (纸本)9789819615278

With the increasing concern for environmental protection and resource optimization, efficient waste sorting has become a serious challenge today. In this paper, we propose a new offloading control problem that aims to solve waste sorting in wireless bin communication networks. Due to limited computational power, bins belonging to embedded devices rely on simple classification models with varying accuracy. In this scenario, consider a network of intelligent bins, each acting as an independent agent capable of deciding to offload an image to the edge server with a more accurate but resource-intensive model when the local classification is deemed inaccurate. thus, Our goal is to find a lightweight online offloading policy that can achieve the best possible sorting accuracy while balancing transmission traffic. the method utilizes a Deep Q-network algorithm that enables each intelligent bin to make image-processing decisions autonomously. In the experiment, we validate the effectiveness of improving the performance of waste classification compared with existing flow control methods. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Distributed Resource Allocation Intelligent Waste Management Waste Sorting Wireless Communication Network

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A Mini-model can Make Machine Unlearning Better 24th

A Mini-model can Make Machine Unlearning Better

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Zhao, Mingkang Yu, Weng Zhu, Congcong China University of Geosciences Wuhan China Ministry of Education Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance School of Information Engineering Beijing China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan572499 China City University of Macau 999078 China

ISBN: (纸本)9789819615247

In recent years, with the development of machine learning, plenty of personal data have been utilized in the training process of the models which incurs severe privacy leakage in the field. Current regulations mandate the removal of private user information from both databases and machine learning models upon specific deletion requests. While wiping data records from memory storage is a straightforward task, eliminating the influence of specific data samples from an already-trained model is challenging. Numerous studies in machine unlearning focus on mitigating the impact of target data by adjusting model parameters in deep learning. However, existing methods for data removal in deep learning often fall short of meeting real-world practicality. the intricacies of deep neural networks make unlearning in deep learning a less expedient process than retraining from scratch. For example, some methods require a ton of matrix calculations or even more complicated computations, and some algorithms need a lot of space, which ends up hindering their effectiveness in real-world applications. Accordingly, we’ve come up with a more practical solution. Our method just needs a small part of the validation dataset to pre-train a mini-model to update the parameters of the target model quickly. With this assistant model, we can quickly and accurately unlearn target classes and items. We prove that our proposed method is highly effective in unlearning tasks with not very large amounts of data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information leakage

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Dynamic Privacy Protection with Large Language Model in Social Networks 24th

Dynamic Privacy Protection with Large Language Model in So...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Xie, Yizhe Zhu, Congcong Zhang, Xinyue Hu, Xiangyu Liu, Xuan School of Data Science City University of Macau Macau China Hainan International College Minzu University of China Hainan China School of Information Engineering Minzu University of China Beijing China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan China

ISBN: (纸本)9789819615445

In contemporary social networks, dynamic privacy protection remains a pivotal yet challenging endeavor due to the intricate and evolving nature of information exchange. Traditional privacy models, predominantly static, falter in effectively safeguarding privacy amidst the complex interplay of continuously changing network interactions and structures. Addressing these deficiencies, this study introduces a novel dynamic privacy protection system anchored by large language model (LLM). Leveraging the natural language processing prowess of LLM, this system excels in real-time, context-sensitive analysis and protection of textual data within vast and variable social networks. By integrating closed-loop control theory, the system adeptly balances robust privacy safeguards with the requisite fluidity of information exchange. Experimental validations on large network datasets illustrate the system’s adeptness in balancing privacy leaks and information distortion through intelligent adaptations to privacy thresholds and strategic noise injection. the outcomes highlight the system’s utility in enhancing data security and operational efficiency, promising significant implications for future applications in broader domains such as mobile computing and IoT. this study not only propels forward the capabilities of dynamic privacy protection mechanisms but also sets a foundational architecture for subsequent innovations in privacy-sensitive, data-intensive environments. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Differential privacy

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Cross-Modal Mask and Detail Alignment for Text-Based Person Retrieval 24th

Cross-Modal Mask and Detail Alignment for Text-Based Perso...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Guo, Ao Liu, Xuan Liu, Xianggan Hu, Bingmeng Yuan, Jie Chu, Chiawei Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance Ministry of Education Minzu University of China Beijing100081 China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan Lingshui Li572499 China Natural Language Processing and Knowledge Graph Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China City University of Macau China

ISBN: (纸本)9789819615278

Text-based person retrieval primarily aims to retrieve the images of target persons represented by a given text query. In this task, how to effectively align images and text globally and locally is an important challenge. At the same time, since multiple modalities are involved, reducing the differences between different modalities is also an important challenge. Existing work has focused more on reconstructing an image rather than the semantic consistency between the image and text modalities. therefore, we introduce the Cross-Modal Mask and Detail Alignment (CMDA) framework to address these challenges. Under this framework, our proposed Cross-Modal Mask Alignment module (CMA) semantically aligns features generated by supplementing randomly masked image/text with another modality text/image. Additionally, to narrow the gap between the image and text modalities, we designed the Cross-Modal Detail Alignment module (CDA), which establishes connections between images and texts and facilitates interactions between these two different modalities. Experimental results show that our model exhibits outstanding performance across multiple public datasets, i.e., CUHK-PEDES, ICFG-PEDES, and RSTPReID. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Semantics

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Performance Evaluation of parallel processing Adder Against Basic Adders on FPGAs 5th

Performance Evaluation of Parallel Processing Adder Against ...

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5th international conference on Computing Science, Communication and Security

作者： Fichadia, Dhaval Purohit, Kishor Soni, Bhavesh Einfochips Ltd Ahmadabad India Ganpat Univ Mehsana India

ISBN: (纸本)9783031751691;9783031751707

Adders are essential components of modern digital circuits, and their primary design goal is to achieve high speed. However, power consumption and chip area are also important considerations in modern circuit design. Optimizing digital adder performance plays a crucial role in enhancing the speed of binary operations within complex circuits. Various architectures address the carry propagation bottleneck, each with its own strengths and weaknesses. Choosing the most appropriate architecture depends on the specific application requirements, ensuring optimal performance within the available resource constraints. this paper provides a comprehensive analysis of various adder topologies and their performance characteristics. By carefully considering the trade-offs between delay, power consumption, and area, engineers can choose the optimal architecture for their specific application requirements, leading to significant improvements in digital system performance and efficiency. the analyzed adder topologies include Ripple Carry Adder (RCA), Carry Lookahead Adder (CLA), Carry Skip Adder (CSK), Carry Select Adder (CSLA), Carry Increment Adder (CIA), Brent kung adder (BKA), Kong stone adder. the analysis is conducted using HDL on the Xilinx ISE 14.7 platform.

关键词： Finite impulse response (FIR) Ripple carry adder (RCA) Carry Look Ahead Adder (CLA) Carry Select Adder (CSLA) Carry Increment Adder (CIA) Carry Skip Adder Kogge Stone Adder (CSKa) Arithmetic-logic unit (ALU) parallel prefix adder (PPA)

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TP-BFT: A Faster Asynchronous BFT Consensus with parallel Structure 24th

TP-BFT: A Faster Asynchronous BFT Consensus with Parallel S...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Ye, Shunliang Chen, Qi Xiao, Fuan Ke, Zhihui Yang, Guoyu Ma, Huawei Institute of Artificial Intelligence Guangzhou University Guangzhou510006 China Guangdong Provincial Key Laboratory of Blockchain Security Guangzhou University Guangzhou510006 China

ISBN: (纸本)9789819615445

the massive success of blockchains has significantly catalyzed interest in the extensive deployment of practical asynchronous Byzantine fault-tolerant (BFT) consensus protocols across wide area networks. However, existing asynchronous consensus protocols suffer from high communication cost and low throughput, which can’t meet the high-performance requirements of blockchain platforms. We identify two key factors that hinder the efficiency of asynchronous BFT protocols: (1) Reliable broadcast protocols incur high communication costs;(2) the broadcast and agreement phases are executed strictly sequentially, resulting in wasted bandwidth resources. To address above issues, we propose the lightweight provable broadcast and a novel asynchronous BFT protocol called TP-BFT. TP-BFT implements a lightweight broadcast followed by a recovery mechanism and supports parallel broadcast, agreement, and recovery phases. this approach offers two key advantages: (1) it mitigates the impact of low-bandwidth nodes on the efficiency of the broadcast phase;(2) it fully utilizes the bandwidth during the agreement phase to enhance the protocol’s throughput. We implement TP-BFT and conduct experiments with HoneyBadger and Dumbo. the results demonstrate that TP-BFT improves throughput by 106% and reduces latency by 64% compared to Dumbo. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bandwidth

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Beqi: Revitalize the Senegalese Wolof Language with a Robust Spelling Corrector 7th

Beqi: Revitalize the Senegalese Wolof Language with a Rob...

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7th EAI international conference on Innovations and Interdisciplinary Solutions for Underserved Areas, InterSol 2024

作者： Mbaye, Derguene Diallo, Moussa Baamtu Dakar Senegal Université Cheikh Anta Diop Dakar Senegal

ISBN: (纸本)9783031864926

the progress of Natural Language processing (NLP), although fast in recent years, is not at the same pace for all languages. African languages in particular are still behind and lack automatic processing tools. Some of these tools are very important for the development of these languages but also have an important role in many NLP applications. this is particularly the case for automatic spell checkers. Several approaches have been studied to address this task and the one modeling spelling correction as a translation task from misspelled (noisy) text to well-spelled (correct) text shows promising results. However, this approach requires a parallel corpus of noisy data on the one hand and correct data on the other hand, whereas Wolof is a low-resource language and does not have such a corpus. In this paper, we present a way to address the constraint related to the lack of data by generating synthetic data and we present sequence-to-sequence models using Deep Learning for spelling correction in Wolof. We evaluated these models in three different scenarios depending on the subwording method applied to the data and showed that the latter had a significant impact on the performance of the models, which opens the way for future research in Wolof spelling correction. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

关键词： Natural language processing systems

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