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A Speculative Loop Pipeline Framework with Accurate Path Modeling for High-Level Synthesis

ACM Transactions on Reconfigurable Technology and Systems 2025年第2期18卷 1-33页

作者： Yuhan She Jierui Liu Yanlong Huang Ray C.C. Cheung Hong Yan City University of Hong Kong Hong Kong China Center for Intelligent Multidimensional Data Analysis City University of Hong Kong Hong Kong China

Loop pipelining is a key optimization in high-level synthesis (HLS), aimed at overlapping the execution of iterations. Static scheduling, dominant in commercial HLS tools, configures the pipeline based on compile-time analysis, proving conservative for designs with irregular control flow and memory access due to imbalanced recurrences. Speculative Loop pipeline (SLP) is a novel concept that addresses the problem by introducing the speculation and recovery mechanism at the source level to improve the throughput. Although proven promising, it has a significant gap from practical application: It requires accurate early-stage modeling of the pipeline configuration for each path, which is unable to obtain with classic HLS scheduling methods because the SLP process itself interferes with the path length. In this work, we made a step forward by proposing a practical SLP framework with accurate path modeling ability through iterative tuning. We further optimize the SLP technology by combining automatic dataflow extraction with speculative source-level transformation to further boost the performance in specific design patterns. Our framework works on the source level and is easy to be plugged into existing downstream HLS tools. Experiment results demonstrate significant performance improvements over commercial HLS tools and better resource trade-offs compared to the state-of-the-art dynamic-scheduling-based solutions.

关键词： high-level synthesis speculative loop pipeline pipelining scheduling FPGA

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LU Decomposition and Generalized Autoone-Takagi Decomposition of Dual Matrices and their Applications

arXiv

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arXiv 2025年

作者： Xu, Renjie Wei, Yimin Yan, Hong Center for Intelligent Multidimensional Data Analysis Hong Kong Science Park Shatin Hong Kong School of Mathematical Sciences Shanghai Key Laboratory of Contemporary Applied Mathematics Fudan University Shanghai200433 China Department of Electrical Engineering Center for Intelligent Multidimensional Data Analysis City University of Hong Kong Hong Kong

This paper uses matrix transformations to provide the Autoone-Takagi decomposition of dual complex symmetric matrices and extends it to dual quaternion η-Hermitian matrices. The LU decomposition of dual matrices is given using the general solution of the Sylvester equation, and its equivalence to the existence of rank-k decomposition and dual Moore-Penrose generalized inverse (DMPGI) is proved. Similar methods are then used to provide the Cholesky decomposition of dual real symmetric positive definite matrices. Both of our decompositions are driven by applications in numerical linear *** Codes 15A23, 15B33, 65F55 Copyright © 2025, The Authors. All rights reserved.

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Gradient neural network models for approximate Tucker decomposition of time-dependent tensors

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Neurocomputing 2025年 640卷

作者： Che, Maolin Wei, Yimin Yan, Hong State Key Laboratory of Public Big Data and School of Mathematics and Statistics Guizhou University Guiyang550025 China Center for Intelligent Multidimensional Data Analysis Hong Science Park Shatin Hong Kong School of Mathematical Sciences and Key Laboratory of Mathematics for Nonlinear Sciences Fudan University Shanghai200433 China Department of Electrical Engineering and Center for Intelligent Multidimensional Data Analysis City University of Hong Kong 83 Tat Chee Avenue Kowloon Hong Kong

In this paper, we introduce a gradient neural network model for approximating the Tucker decomposition of time-dependent tensors with a specified multilinear rank bound. The behavior of this model is governed by a system of ordinary differential equations. First, based on the Karush–Kuhn–Tucker (KKT) conditions of the optimization problem for the approximate Tucker decomposition of a constant tensor, a gradient neural network model is proposed, demonstrating local stability according to Lyapunov stability theory. Next, we design a gradient neural network model specifically tailored for time-dependent tensors. Finally, using the Taylor-type difference rules, we discretize the proposed neural network models, ensuring zero stability for the implementation of digital hardware. We illustrate the effectiveness and characteristics of our approach through numerical examples. © 2025 Elsevier B.V.

关键词： Lyapunov methods

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An effective method for quantification,visualization,and analysis of 3D cell shape during early embryogenesis

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Quantitative Biology 2025年第1期13卷 113-126页

作者： Zelin Li Zhaoke Huang Jianfeng Cao Guoye Guan Zhongying Zhao Hong Yan Department of Electrical Engineering City University of Hong KongHong KongChina Centre for Intelligent Multidimensional Data Analysis Limited Hong KongChina Center for Quantitative Biology Peking UniversityBeijingBeijingChina Department of Systems Biology Harvard Medical SchoolBostonMassachusettsUSA Department of Data Science Dana-Farber Cancer InstituteBostonMassachusettsUSA Department of Biology Hong Kong Baptist UniversityHong KongChina State Key Laboratory of Environmental and Biological Analysis Hong Kong Baptist UniversityHong KongChina

Embryogenesis is the most basic process in developmental *** and simply quantifying cell shape is challenging for the complex and dynamic 3D embryonic *** descriptors such as volume,surface area,and mean curvature often fall short,providing only a global view and lacking in local detail and reconstruction *** this,we introduce an effective integrated method,3D Cell Shape Quantification(3DCSQ),for transforming digitized 3D cell shapes into analytical feature vectors,named eigengrid(proposed grid descriptor like eigen value),eigenharmonic,and *** uniquely combine spherical grids,spherical harmonics,and principal component analysis for cell shape *** demonstrate 3DCSQ’s effectiveness in recognizing cellular morphological phenotypes and clustering *** to Caenorhabditis elegans embryos of 29 living embryos from 4-to 350-cell stages,3DCSQ identifies and quantifies biologically reproducible cellular patterns including distinct skin cell *** also provide automatically cell shape lineaging analysis *** method not only systematizes cell shape description and evaluation but also monitors cell differentiation through shape changes,presenting an advancement in biological imaging and analysis.

关键词： Caenorhabditis elegans(C.elegans) cell shape quantification eigen features(eigengrid eigenharmonic&eigenspectrum) lineage analysis morphological reproducibility spherical harmonics(SPHARM)

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data Resolution and Future Challenges in Automated Undersea Cable System Design

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IEEE Network 2025年

作者： Wang, Tianjiao Wang, Xinyu Moran, Bill Wang, Zengfu Zukerman, Moshe Hong Kong Science Park Center for Intelligent Multidimensional Data Analysis Hong Kong Hong Kong The University of Melbourne Department of Electrical and Electronic Engineering MelbourneVIC3010 Australia Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China Northwestern Polytechnical University School of Automation Xi’an710072 China City University of Hong Kong Department of Electrical Engineering Kowloon Hong Kong

Undersea optical cables form the backbone of global Internet infrastructure, carrying nearly 99% of international Internet data. As demand for high-speed and reliable connectivity continues to grow, optimizing undersea cable path planning has become essential for reducing costs, mitigating risks, and ensuring high-quality service delivery. While traditional manual planning methods remain widely used by cable system designers, surveyors, and planners, the increasing complexity of cable deployment necessitates the integration of automated methods that leverage advanced computational techniques to enhance efficiency and decision-making. This paper reviews the state-of-the-art in cable path planning and system design, emphasizing the important role of data density in achieving optimal cable paths. In particular, we present realistic path-planning scenarios on the Earth’s surface and describe the Fast Marching Method (FMM), a computational approach that efficiently determines optimal routing based on available data. Simulation results show that increasing map resolution, along with accompanying data at the higher resolution, significantly enhances path planning accuracy, reduces deployment costs, and improves risk assessment, underscoring the importance of acquiring and utilizing high-density data. This understanding leads to a discussion of the challenges of data acquisition and computational scalability in processing large-scale datasets, and the importance of collaboration between cable system engineers, geospatial data scientists, and software developers. This paper proposes a collaborative framework in which automated algorithms complement traditional planning methods, enabling cost-effective solutions that continually adapt and become more effective through expert feedback. This research contributes to a more resilient, efficient, and adaptive global undersea communication network, paving the way for the next generation of intelligent, optimized cable infrastructur

关键词： Risk assessment

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An effective method for quantification, visualization, and analysis of 3D cell shape during early embryogenesis

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Quantitative Biology 2025年第01期 134-147页

作者： Zelin Li Zhaoke Huang Jianfeng Cao Guoye Guan Zhongying Zhao Hong Yan Department of Electrical Engineering City University of Hong Kong Centre for Intelligent Multidimensional Data Analysis Limited Center for Quantitative Biology Peking University Department of Systems Biology Harvard Medical School Department of Data Science Dana-Farber Cancer Institute Department of Biology Hong Kong Baptist University State Key Laboratory of Environmental and Biological Analysis Hong Kong Baptist University

Embryogenesis is the most basic process in developmental *** and simply quantifying cell shape is challenging for the complex and dynamic 3D embryonic cells. Traditional descriptors such as volume,surface area, and mean curvature often fall short, providing only a global view and lacking in local detail and reconstruction capability. Addressing this, we introduce an effective integrated method, 3D Cell Shape Quantification(3DCSQ), for transforming digitized 3D cell shapes into analytical feature vectors, named eigengrid(proposed grid descriptor like eigen value), eigenharmonic, and eigenspectrum. We uniquely combine spherical grids, spherical harmonics, and principal component analysis for cell shape quantification. We demonstrate 3DCSQ's effectiveness in recognizing cellular morphological phenotypes and clustering cells. Applied to Caenorhabditis elegans embryos of 29 living embryos from 4-to 350-cell stages,3DCSQ identifies and quantifies biologically reproducible cellular patterns including distinct skin cell deformations. We also provide automatically cell shape lineaging analysis program. This method not only systematizes cell shape description and evaluation but also monitors cell differentiation through shape changes, presenting an advancement in biological imaging and analysis.

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Cross-Domain Object Classification Via Successive Subspace Alignment 48

Cross-Domain Object Classification Via Successive Subspace A...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chen, Kecheng Li, Haoliang Yan, Hong City University of Hong Kong Department of Electrical Engineering Center for Intelligent Multidimensional Data Analysis Kowloon Hong Kong

ISBN: (纸本)9781728163277

Recently, successive subspace learning (SSL)-based methods have shown to be effective for the task of visual object classification with mild data desire and mathematically transparent interpretable capability. However, existing SSL-based methods rely heavily on the data-centric subspace representations, leading to potential performance degradation problem in case of the domain shift between the training (a.k.a., source domain) and testing (a.k.a., target domain) data. To address this limitation, we propose an effective successive subspace learning method based on existing SSL-based methods. Specifically, we introduce a novel linear transformation layer to align eigenvectors in SSL module between source and target domains, as such, the discrepancy between source and target domains will be reduced, resulting in better cross-domain performance. The effectiveness of our proposed method is demonstrated on the Office-Caltech-10 and Office-31 benchmark datasets by using features extracted from pre-trained deep neural networks as input. © 2023 IEEE.

关键词： Linear transformations

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Co-clustering for coherent pattern detection

Co-clustering for coherent pattern detection

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Yan, Hong Wong Chun Hong Professor of Data Engineering City University of Hong Kong Hong Kong Center for Intelligent Multidimensional Data Analysis Limited

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A Research of a Modified Bearing Fault Diagnosis Model Based on Deformable Atrous Convolution and Squeeze-and-Excitation Aggregation 13

A Research of a Modified Bearing Fault Diagnosis Model Based...

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13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022

作者： Yu, Y. Chen, Z.Y. Fang, X. Zhang, D.C. Wang, Z. School of Mechanical Engineering Sichuan University Chengdu China City University of Hong Kong Hong Kong and Center for Intelligent Multidimensional Data Analysis Hong Kong Science Park Department of Advanced Design and Systems Engineering Shatin Hong Kong

ISBN: (数字)9781665486903

ISBN: (纸本)9781665486903

In order to accurately and intelligently identify the fault signal characteristics of bearing in modern rotating machinery industries, a comprehensive novel fault diagnosis model is introduced by using a few data points as input signals. In this paper, the main methods of the model are processing the two signal inputs, frequency-domain signal (FDS) and time-frequency graph (TFG), into a network built up with convolution (Conv) and deformable atrous Conv. Then, this network extracts their features respectively. After squeezing-and-excitation aggregation on the features, three types of outputs are obtained for tasks of faulty bearing position detection, fault type diagnosis, and damage size estimation. This method allows the fault diagnosis of different locations, fault types, and fault degrees to be completed simultaneously, and the state-of-Art effect can be achieved. Two bearing vibration signal datasets are used in the paper to evaluate the performance of the model, and experimental results prove an effective multi-task fault diagnosis ability on the three tasks. © 2022 IEEE.

关键词： Convolution

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Cross-Domain Object Classification Via Successive Subspace Alignment

Cross-Domain Object Classification Via Successive Subspace A...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Kecheng Chen Haoliang Li Hong Yan Department of Electrical Engineering and Center for Intelligent Multidimensional Data Analysis City University of Hong Kong Kowloon Hong Kong SAR

关键词： Training Learning systems Degradation Deep learning Visualization Neural networks Signal processing

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