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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Zeng, Yan Huang, Chengchuang Ni, Yijie Zhou, Chunbao Zhang, Jilin Wang, Jue Zhou, Mingyao Xue, Meiting Zhang, Yunquan Hangzhou Dianzi University School of Computer Science and Technology Hangzhou310018 China Ministry of Education Key Laboratory for Modeling and Simulation of Complex Systems Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Hangzhou Dianzi University School of ITMO Joint Institute Hangzhou310018 China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing100086 China HuaWei China Institute of Computing Technology of the Chinese Academy of Sciences State Key Laboratory of Computer Architecture Beijing100086 China

ISBN: (纸本)9798350330717

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network's dataflow graph and device cluster's topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method. © 2023 IEEE.

关键词： Auto-Parallel Distributed machine learning Genetic algorithm Integer linear programming Model parallelism

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SP3: Enhancing Structured Pruning via PCA Projection

arXiv

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arXiv 2023年

作者： Hu, Yuxuan Zhang, Jing Zhao, Zhe Zhao, Chen Chen, Xiaodong Li, Cuiping Chen, Hong School of Information Renmin University of China Beijing China Key Laboratory of Data Engineering and Knowledge Engineering MOE China Engineering Research Center of Database and Business Intelligence MOE China Tencent AI Lab Tencent Beijing China School of Computer Science and Technology Xi'an Jiaotong University Xi'An China

Structured pruning is a widely used technique for reducing the size of pre-trained language models (PLMs), but current methods often overlook the potential of compressing the hidden dimension (d) in PLMs, a dimension critical to model size and efficiency. This paper introduces a novel structured pruning approach, Structured Pruning with PCA Projection (SP3), targeting the effective reduction of d by projecting features into a space defined by principal components before masking. Extensive experiments on benchmarks (GLUE and SQuAD) show that SP3 can reduce d by 70%, compress 94% of the BERTbase model, and maintain over 96% accuracy and outperform other methods that compress d by 6% in accuracy at the same compression ratio. SP3 has also proven effective with other models, including OPT and Llama. Our data and code are available at ours repo. © 2023, CC BY.

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Genetic Auto-prompt Learning for Pre-trained Code Intelligence Language Models

arXiv

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arXiv 2024年

作者： Feng, Chengzhe Sun, Yanan Li, Ke Zhou, Pan Lv, Jiancheng Lu, Aojun The College of Computer Science Sichuan University Chengdu610065 China The Department of Computer Science University of Exeter ExeterEX4 4QF United Kingdom The Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China

As Pre-trained Language Models (PLMs), a popular approach for code intelligence, continue to grow in size, the computational cost of their usage has become prohibitively expensive. Prompt learning, a recent development in the field of natural language processing, emerges as a potential solution to address this challenge. In this paper, we investigate the effectiveness of prompt learning in code intelligence tasks. We unveil its reliance on manually designed prompts, which often require significant human effort and expertise. Moreover, we discover existing automatic prompt design methods are very limited to code intelligence tasks due to factors including gradient dependence, high computational demands, and limited applicability. To effectively address both issues, we propose Genetic Auto Prompt (GenAP), which utilizes an elaborate genetic algorithm to automatically design prompts. With GenAP, non-experts can effortlessly generate superior prompts compared to meticulously manual-designed ones. GenAP operates without the need for gradients or additional computational costs, rendering it gradient-free and cost-effective. Moreover, GenAP supports both understanding and generation types of code intelligence tasks, exhibiting great applicability. We conduct GenAP on three popular code intelligence PLMs with three canonical code intelligence tasks including defect prediction, code summarization, and code translation. The results suggest that GenAP can effectively automate the process of designing prompts. Specifically, GenAP outperforms all other methods across all three tasks (e.g. improving accuracy by an average of 2.13% for defect prediction). To the best of our knowledge, GenAP is the first work to automatically design prompts for code intelligence PLMs. Copyright © 2024, The Authors. All rights reserved.

关键词： Design

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Energy Efficiency Maximization in RISs-Assisted UAVs-Based Edge Computing Network Using Deep Reinforcement Learning

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Big data Mining and Analytics 2024年第4期7卷 1065-1083页

作者： Chuanwen Luo Jian Zhang Jianxiong Guo Yi Hong Zhibo Chen Shuyang Gu School of Information Science and Technology Beijing Forestry UniversityBeijing 100083Chinaand also with the Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland AdministrationBeijing 100083China Advanced Institute of Natural Sciences Beijing Normal UniversityZhuhai 519087Chinaand also with the Guangdong Key Lab of AI and Multi-Modal Data ProcessingBNU-HKBU United International CollegeZhuhai 519087China Department of Computer Information Systems Texas A&M University-Central TexasKilleenTX 76549USA

Edge Computing(EC)pushes computational capability to the Terrestrial Devices(TDs),providing more efficient and faster computing *** Aerial Vehicles(UAVs)equipped with EC servers can be flexibly deployed,even in complex terrains,to provide mobile computing services at all ***,UAVs can establish an air-to-ground line-of-sight link with TDs to improve the quality of communication ***,the UAV-to-TD link may be obstructed by ground obstacles such as buildings or trees,leading to sub-optimal data transmission *** surmount this issue,Reconfigurable Intelligent Surfaces(RISs)emerge as a promising technology capable of intelligently reflecting signals to enhance communication quality between UAVs and *** this paper,we consider the RISs-assisted multi-UAVs collaborative edge Computing Network(RUCN)in urban environment,in which we study the computational offloading *** goal is to maximize the overall energy efficiency of UAVs by jointly optimizing the flight duration and trajectories of UAVs,and the phase shifts of *** is worth noting that this problem has been formally established as ***,we propose the Deep Deterministic Policy Gradients based UAV Trajectory and RIS Phase shift optimization algorithm(UTRP-DDPG)to solve this complex optimization *** results of extensive numerical experiments show that the proposed algorithm outperforms the other benchmark algorithms under various parameter ***,the UTRP-DDPG algorithm improves the UAV energy efficiency by at least 2%compared to DQN algorithm.

关键词： edge computing computing offloading Unmanned Aerial Vehicle(UAV) Reconfigurable Intelligent Surface(RIS) deep reinforcement learning

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SpreadFGL: Edge-Client Collaborative Federated Graph Learning with Adaptive Neighbor Generation

SpreadFGL: Edge-Client Collaborative Federated Graph Learnin...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Luying Zhong Yueyang Pi Zheyi Chen Zhengxin Yu Wang Miao Xing Chen Geyong Min College of Computer and Data Science Fuzhou University China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University China Engineering Research Center of Big Data Intelligence Ministry of Education China School of Computing and Communications University of Lancaster UK School of Engineering Computing and Mathematics University of Plymouth UK Department of Computer Science University of Exeter UK

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Federated Graph Learning (FGL) has garnered widespread attention by enabling collaborative training on multiple clients for semi-supervised classification tasks. However, most existing FGL studies do not well consider the missing inter-client topology information in real-world scenarios, causing insufficient feature aggregation of multi-hop neighbor clients during model training. Moreover, the classic FGL commonly adopts the FedAvg but neglects the high training costs when the number of clients expands, resulting in the overload of a single edge server. To address these important challenges, we propose a novel FGL framework, named SpreadFGL, to promote the information flow in edge-client collaboration and extract more generalized potential relationships between clients. In SpreadFGL, an adaptive graph imputation generator incorporated with a versatile assessor is first designed to exploit the potential links between subgraphs, without sharing raw data. Next, a new negative sampling mechanism is developed to make SpreadFGL concentrate on more refined information in downstream tasks. To facilitate load balancing at the edge layer, SpreadFGL follows a distributed training manner that enables fast model convergence. Using real-world testbed and benchmark graph datasets, extensive experiments demonstrate the effectiveness of the proposed SpreadFGL. The results show that SpreadFGL achieves higher accuracy and faster convergence against state-of-the-art algorithms.

关键词： Training Accuracy Collaboration Benchmark testing Imputation Generators Topology

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Global Context Aggregation Network for Lightweight Saliency Detection of Surface Defects

arXiv

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arXiv 2023年

作者： Yan, Feng Jiang, Xiaoheng Lu, Yang Cui, Lisha Li, Shupan Cao, Jiale Xu, Mingliang Tao, Dacheng School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China Engineering Research Center of Intelligent Swarm Systems Ministry of Education Zhengzhou China National Supercomputing Center in Zhengzhou Zhengzhou China School of Electrical and Information Engineering Tianjin University Tianjin China the Sydney AI Centre the School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia

Surface defect inspection is a very challenging task in which surface defects usually show weak appearances or exist under complex backgrounds. Most high-accuracy defect detection methods require expensive computation and storage overhead, making them less practical in some resource-constrained defect detection applications. Although some lightweight methods have achieved real-time inference speed with fewer parameters, they show poor detection accuracy in complex defect scenarios. To this end, we develop a Global Context Aggregation Network (GCANet) for lightweight saliency detection of surface defects on the encoder-decoder structure. First, we introduce a novel transformer encoder on the top layer of the lightweight backbone, which captures global context information through a novel Depthwise Self-Attention (DSA) module. The proposed DSA performs element-wise similarity in channel dimension while maintaining linear complexity. In addition, we introduce a novel Channel Reference Attention (CRA) module before each decoder block to strengthen the representation of multi-level features in the bottom-up path. The proposed CRA exploits the channel correlation between features at different layers to adaptively enhance feature representation. The experimental results on three public defect datasets demonstrate that the proposed network achieves a better trade-off between accuracy and running efficiency compared with other 17 state-of-the-art methods. Specifically, GCANet achieves competitive accuracy (91.79% Fβw, 93.55% Sα, and 97.35% E) on SD-saliency-900 while running 272fps on a single gpu. Copyright © 2023, The Authors. All rights reserved.

关键词： Defects

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JMNet: A joint matting network for automatic human matting

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Computational Visual Media 2020年第2期6卷 215-224页

作者： Xian Wu Xiao-Nan Fang Tao Chen Fang-Lue Zhang Department of Computer Science and Technology Tsinghua UniversityBeijing100084China AI Center at Visual China Group BurlingameCA94010USA School of Engineering and Computer Science Victoria University of WellingtonWellingtonNew Zealand

We propose a novel end-to-end deep learning framework, the Joint Matting Network(JMNet), to automatically generate alpha mattes for human *** utilize the intrinsic structures of the human body as seen in images by introducing a pose estimation module,which can provide both global structural guidance and a local attention focus for the matting task. Our network model includes a pose network, a trimap network, a matting network, and a shared encoder to extract features for the above three networks. We also append a trimap refinement module and utilize gradient loss to provide a sharper alpha matte. Extensive experiments have shown that our method outperforms state-of-theart human matting techniques;the shared encoder leads to better performance and lower memory *** model can process real images downloaded from the Internet for use in composition applications.

关键词： alpha matting human images deep learning pose estimation

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Mixed Attention Densely Residual Network for Single Image Super-Resolution

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computer Systems science & engineering 2021年第10期39卷 133-146页

作者： Jingjun Zhou Jing Liu Jingbing Li Mengxing Huang Jieren Cheng Yen-Wei Chen Yingying Xu Saqib Ali Nawaz School of Information and Communication Engineering Hainan UniversityHaikou570228China State Key Laboratory of Marine Resource Utilization in the South China Sea Hainan UniversityHaikou570228China Research Center for Healthcare Data Science Zhejiang LabHangzhou311121China School of Computer Science and Cyberspace Security Hainan UniversityHaikou570228China Graduate School of Information Science and Engineering Ritsumeikan University5258577Japan College of Computer Science and Technology Zhejiang UniversityHangzhou311100China

Recent applications of convolutional neural networks(CNNs)in single image super-resolution(SISR)have achieved unprecedented ***,existing CNN-based SISR network structure design consider mostly only channel or spatial information,and cannot make full use of both channel and spatial information to improve SISR performance *** present work addresses this problem by proposing a mixed attention densely residual network architecture that can make full and simultaneous use of both channel and spatial ***,we propose a residual in dense network structure composed of dense connections between multiple dense residual groups to form a very deep *** structure allows each dense residual group to apply a local residual skip connection and enables the cascading of multiple residual blocks to reuse previous features.A mixed attention module is inserted into each dense residual group,to enable the algorithm to fuse channel attention with laplacian spatial attention effectively,and thereby more adaptively focus on valuable feature *** qualitative and quantitative results of extensive experiments have demonstrate that the proposed method has a comparable performance with other stateof-the-art methods.

关键词： Channel attention Laplacian spatial attention residual in dense mixed attention

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A Scalable, Efficient, and Robust Dynamic Memory Management Library for HLS-based FPGAs

A Scalable, Efficient, and Robust Dynamic Memory Management ...

引用

IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Qinggang Wang Long Zheng Zhaozeng An Shuyi Xiong Runze Wang Yu Huang Pengcheng Yao Xiaofei Liao Hai Jin Jingling Xue National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Zhejiang Lab Hangzhou China School of Computer Science and Engineering University of New South Wales Sydney NSW Australia

ISBN: (数字)9798350350579

ISBN: (纸本)9798350350586

Nowadays, high-level synthesis (HLS) has gained prominence for FPGA-based architecture prototyping, enhancing productivity significantly. Despite this advancement, HLS tools are impeded by a critical drawback: they lack support for dynamic memory management (DMM), leading to static mem-ory allocation and suboptimal use of memory resources. In response, numerous efforts have been made to develop DMM solutions compatible with HLS. However, our analysis indicates that existing solutions fail to concurrently meet the desired trifecta of scalability (efficient management of memory of any size), efficiency (minimal latency in memory (de-)allocation), and robustness (low allocation failure rates). This limitation hampers their applicability in real-world scenarios. In this paper, we introduce GraDMM, a “three-birds-one- stone” solution that comprehensively enhances the scalability, efficiency, and robustness of DMM. The key insight is to formulate memory (de-)allocation as graph analytics and lever-age sophisticated FPGA-based graph processing techniques. To achieve scalability, GraDMM specializes a simplified pipeline that significantly suppresses resource utilization expansion caused by managed memory scaling. This is crucial for managing arbitrarily sized memory on resource-limited FPGA platforms. For efficiency, GraDMM implements a data-centric concurrent traversal scheme and a shortcut-assisted fast traversal policy to accelerate (de-)allocation-guided graph traversal, reducing mem-ory (de-)allocation latency. To enhance robustness, GraDMM incorporates an adaptive memory defragmenter that defragments managed memory to minimize fragmentation-induced allocation failures. GraDMM is encapsulated as a library, providing high- level interfaces for users and ensuring synthesizability with Vi- vado HLS. Experimental results demonstrate that GraDMM out-performs three state-of-the-art HLS-compatible DMM solutions by significant margins: 56.71 %-85.59% in resource consumption sa

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MorphGrower: a synchronized layer-by-layer growing approach for plausible neuronal morphology generation 24

MorphGrower: a synchronized layer-by-layer growing approach ...

引用

Proceedings of the 41st International Conference on Machine Learning

作者： Nianzu Yang Kaipeng Zeng Haotian Lu Yexin Wu Zexin Yuan Danni Chen Shengdian Jiang Jiaxiang Wu Yimin Wang Junchi Yan School of Artificial Intelligence & Department of Computer Science and Engineering & MoE Lab of AI Shanghai Jiao Tong University Guangdong Institute of Intelligence Science and Technology SEU-ALLEN Joint Center Institute for Brain and Intelligence Southeast University XVERSE Technology

Neuronal morphology is essential for studying brain functioning and understanding neurodegenerative disorders. As acquiring real-world morphology data is expensive, computational approaches for morphology generation have been studied. Traditional methods heavily rely on expert-set rules and parameter tuning, making it difficult to generalize across different types of morphologies. Recently, MorphVAE was introduced as the sole learning-based method, but its generated morphologies lack plausibility, i.e., they do not appear realistic enough and most of the generated samples are topologically invalid. To fill this gap, this paper proposes MorphGrower, which mimicks the neuron natural growth mechanism for generation. Specifically, MorphGrower generates morphologies layer by layer, with each subsequent layer conditioned on the previously generated structure. During each layer generation, MorphGrower utilizes a pair of sibling branches as the basic generation block and generates branch pairs synchronously. This approach ensures topological validity and allows for fine-grained generation, thereby enhancing the realism of the final generated morphologies. Results on four real-world datasets demonstrate that MorphGrower outperforms MorphVAE by a notable margin. Importantly, the electrophysiological response simulation demonstrates the plausibility of our generated samples from a neuroscience perspective. Our code is available at https://***/Thinklab-SJTU/MorphGrower.

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