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Acceleration of an Optimized Kvazaar All Intra Prediction on Embedded Systems Based on the Directional Texture Complexity

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IEEE EMBEDDED SYSTEMS LETTERS 2025年第1期17卷 38-41页

作者： Majok, James R. Abo-Zahhad, Mohammed Inoue, Koji Sayed, Mohammed S. Egypt Japan Univ Sci & Technol Dept Elect & Commun Engn Alexandria 21934 Egypt Kyushu Univ Fac Informat Sci & Elect Engn Fukuoka 8190395 Japan

The high growth of real-time video applications on embedded systems poses challenges for practical encoders aiming to deliver high quality and high speed simultaneously. In real-world video applications, the slower preset in Kvazaar HEVC encoder can achieve impressive quality, with a penalty of extensive computational time. This is ultimately due to rate-distortion optimization that involves a comprehensive analysis of all possible quad-tree partitioning within the coding tree unit (CTU) structure, resulting in unpleasant encoding complexity. This letter proposes a method of accelerating All Intraprediction on Nvidia Jetson TX1 using early termination of CTU partitioning and a method of selecting only eight modes for intraframe search. The proposed technique reduces the running time of an optimized Kvazaar all intraprediction by 48.4% and 40.24% at slower and higher presets, respectively, with an average BD rate lost of 1.5% and 0.682% compared to the optimized Kvazaar running under the same coding configuration.

关键词： Encoding Complexity theory partitioning algorithms Embedded systems Visualization Streaming media Standards All intra coding tree unit (CTU) partition Kvazaar HEVC encoder rough mode decision (RMD) texture-based complexity

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A Hardware-Friendly Lightweight Partition Decision Algorithm for VVC Intra and Inter Coding

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IEEE SIGNAL PROCESSING LETTERS 2025年 32卷 941-945页

作者： Zan, Zhao Huang, Leilei Chen, Shushi Zeng, Xiaoyang Fan, Yibo Fudan Univ State Key Lab Integrated Chips & Syst Shanghai 201203 Peoples R China East China Normal Univ Inst Microelect Circuits & Syst Shanghai 200241 Peoples R China

The Versatile Video Coding (VVC) standard notably enhances encoding efficiency with the Quad-Tree plus Multi-Type Tree (QTMTT) partition structure. However, the complex QTMTT tool presents substantial challenges in both software and hardware implementation. To overcome those challenges, this paper introduces a hardware-friendly partition decision algorithm for VVC intra and inter coding. Firstly, we propose a lightweight backbone network to extract partition-aware features. Secondly, we employ a Quantisation Parameter (QP) fusion network to regulate the impact of QPs on the partition structure. Additionally, we apply a top-down threshold-driven post-processing algorithm, in which improbable partition types are removed to directly derive the unique partition structure. Experiments show that our method not only exceeds the previous state-of-the-art work in BD-BR performance, but also shows sufficient hardware-friendly characteristics. To the best of our knowledge, this work is among the earliest to comprehensively discuss and implement a hardware-friendly partition decision algorithm.

关键词： partitioning algorithms Hardware Encoding Signal processing algorithms Prediction algorithms Feature extraction Complexity theory Software algorithms Convolution Software VVC QTMTT partition decision complexity reduction hardware-friendly algorithm convolutional neural network intra coding inter coding

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Exploring Evolutionary Spectral Clustering for Temporal-Smoothed Clustered Cell-Free Networking

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IEEE WIRELESS COMMUNICATIONS LETTERS 2025年第2期14卷 494-498页

作者： Wang, Junyuan Wu, Tianyao Zhou, Ouyang Zhu, Yaping Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China Tongji Univ Sch Comp Sci & Technol Shanghai 201804 Peoples R China

Clustered cell-free networking, which dynamically partitions the whole network into nonoverlapping subnetworks, has been recently proposed to mitigate the cell-edge problem in cellular networks. However, prior works only focused on optimizing clustered cell-free networking in static scenarios with fixed users. This could lead to a large number of handovers in the practical dynamic environment with moving users, seriously hindering the implementation of clustered cell-free networking in practice. This letter considers user mobility and aims to simultaneously maximize the sum rate and minimize the number of handovers. By transforming the multi-objective optimization problem into a time-varying graph partitioning problem and exploring evolutionary spectral clustering, a temporal-smoothed clustered cell-free networking algorithm is proposed, which is shown to be effective in smoothing network partitions over time and reducing handovers while maintaining similar sum rate.

关键词： Handover Optimization Clustering algorithms Cellular networks partitioning algorithms Transforms Uplink Signal to noise ratio Precoding Heuristic algorithms Clustered cell-free networking user mobility temporal smoothness handover evolutionary spectral clustering

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Nonparametric Bootstrap Likelihood Estimation to Investigate the Chance Set-Up on Clustering Results

IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY

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IEEE OPEN JOURNAL OF THE COMPUTER SOCIETY 2025年第1期6卷 438-448页

作者： Elnour, Ammar Yang, Wencheng Li, Yan Univ Southern Queensland Sch Math Phys & Comp Toowoomba Qld 4350 Australia

Clustering algorithms are widely used in the knowledge discovery domain, but concerns and questions about the validity of the results must be considered. The datasets commonly used for clustering tasks are often large and scale-free, making conventional statistical techniques inadequate for analyzing result uncertainty. This issue applies to most outcomes obtained from other knowledge discovery techniques, such as machine learning and statistical learning. Traditional statistical methods assume data follows standard distributions, whereas resampling and bootstrapping methods offer more accurate and reliable alternatives. This article introduces a method that employs bootstrap likelihood estimation to infer the uncertainty of generated clustering structures. We first calculated the clustering error in the original dataset and then utilized the proposed method to estimate its nonparametric bootstrapped likelihood. By comparing these two values, we can establish a nonparametric significance testing framework that directly determines the validity of the result. To evaluate the effectiveness of our method, we conducted experiments using synthetic and real datasets. The results demonstrate that our method can successfully validate clustering results.

关键词： Uncertainty Clustering algorithms Machine learning Estimation Statistical learning Surveys Standards Noise measurement Testing partitioning algorithms Clustering validity testing randomness bootstrap likelihood significance testing statistical machine learning

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Cluster Sort: A Novel Hybrid Approach to Efficient In-Place Sorting Using Data Clustering

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IEEE ACCESS 2025年 13卷 74359-74374页

作者： Subramaniam, Mohit Tripathi, Tanya Chandraumakantham, Omkumar Vellore Inst Technol Sch Comp Sci & Engn Chennai 600127 Tamil Nadu India

This research work introduces a clustering-based in-place sorting algorithm, cluster sort. It is designed in such a way that it improves sorting efficiency by using data locality. It works in two phases: first, data elements are clustered based on the similarity in the values and then each of these clusters is sorted independently using comb or shell sort. This is a hybrid approach, and it helps minimize multiple comparisons within the clusters, which in turn improves performance, especially in large datasets with specific distributions. The traditional algorithms were selected due to their well-known efficiency and widespread use in sorting large datasets. This experiment of ours includes ordered, reverse-ordered, Gaussian, repeated values, same values, and uniform distributions. The results acquired from this experiment show that Cluster Sort (comb) outperforms Cluster Sort (shell) by 88% in ordered datasets and is 92.32% faster than Merge Sort. For reverse-ordered datasets, Cluster Sort (Comb) is 70.79% faster than Bucket Sort and 90.69% faster than Cluster Sort (Shell). In Gaussian distributions, Cluster Sort (Shell) improves by 25.88% over Bucket Sort and 74.41% over Merge Sort. In repeated-value datasets, although Quick Sort is faster, Cluster Sort (Shell) surpasses Bucket Sort by 13.64%. For uniform distributions, Cluster Sort (Shell) is 30.28% faster than Bucket Sort and 75% faster than Merge Sort. The reduction in unwanted comparisons through clustering is essentially what made Cluster Sort significantly outperform traditional algorithms in these datasets with inherent patterns, making Cluster Sort a coherent choice for practical applications.

关键词： Clustering algorithms Sorting Heuristic algorithms partitioning algorithms Memory management Merging Complexity theory Random access memory Machine learning algorithms Time complexity Merge sort sorting algorithms shell sort bucket sort heap sort comb sort clustering

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A Lasserre SDP Rounding Approximation Algorithm for Max Directed 3-Section

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TSINGHUA SCIENCE AND TECHNOLOGY 2025年第4期30卷 1885-1896页

作者： Li, Guangfeng Sun, Jian Du, Donglei Zhang, Xiaoyan Nanjing Normal Univ Sch Math Sci Nanjing 210023 Peoples R China Nanjing Normal Univ Inst Math Nanjing 210023 Peoples R China Nanjing Normal Univ Minist Educ Numer Simulat Large Scale Complex Syst Key Lab Nanjing 210023 Peoples R China Nankai Univ Ctr Combinator Tianjin 300000 Peoples R China Nankai Univ LPMC Tianjin 300000 Peoples R China Univ New Brunswick Fac Management Fredericton NB E3B5A3 Canada

We consider the Max Directed 3-Section problem, which is closely connected to other well-known graph partition problems, such as Max Cut and Max Bisection. Given an arc-weighted directed graph, the goal of the Max Directed 3-Section problem is to partition the vertex set into three disjoint subsets with equal size, while maximizing the total weight of arcs crossing different vertex subsets. By combining the Lasserre hierarchy with the random hyperplane rounding strategy, we propose a polynomial-time algorithm with approximation ratio of 0.489.

关键词： Directed graphs Programming Approximation algorithms partitioning algorithms MATLAB Semi-Definite Programming (SDP) Lasserre hierarchy approximation algorithm Max Cut Max Bisection

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A Short Tutorial: Some Things You Might Not Know About k-Means Clustering

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IEEE SYSTEMS MAN AND CYBERNETICS MAGAZINE 2025年第1期11卷 23-33页

作者： Bezdek, James C. Cornell Univ Ithaca NY 14850 USA

The k-means model and algorithms to optimize it are ubiquitous in cluster analysis. It is impossible to overstate the popularity of this method, which is by far the most heavily cited and studied approach to hard (i.e., non-soft) clustering on earth. This limited tutorial dispels some popularly held misconceptions about this basic method. It begins with a short history of the two algorithms commonly called k-means. Then the geometric structure of hard and soft partition sets underlying all hard, probabilistic, and fuzzy clustering algorithms is presented. The structural theory illuminates some little-known facts about the foundations of k-means and some of its soft relatives. Finally, two soft (probabilistic and fuzzy) generalizations of k-means that should be of interest to practitioners in this area are briefly discussed.

关键词： Earth Analytical models Clustering algorithms Tutorials Probabilistic logic partitioning algorithms History Cybernetics

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partitioning - AN ESSENTIAL STEP IN MAPPING algorithms INTO SYSTOLIC ARRAY PROCESSORS

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COMPUTER 1987年第7期20卷 77-89页

作者： NAVARRO, JJ LLABERIA, JM VALERO, M Facultad de Inform?tica de Barcelona Universidad Polit?cnica de Catalu?

First Page of the Article

关键词： partitioning algorithms Systolic arrays Signal processing algorithms Equations Fault tolerant systems Computational modeling Design methodology Hardware Digital signal processing Algorithm design and analysis

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K-SpecPart: Supervised Embedding algorithms and Cut Overlay for Improved Hypergraph partitioning

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IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2024年第4期43卷 1232-1245页

作者： Bustany, Ismail Kahng, Andrew B. Koutis, Ioannis Pramanik, Bodhisatta Wang, Zhiang Adv Micro Devices Inc Adapt & Embedded Comp Grp AECG Santa Clara CA 95054 USA Univ Calif San Diego Dept Comp Sci & Engn La Jolla CA 92093 USA Univ Calif San Diego Dept Elect & Comp Engn La Jolla CA 92093 USA New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA Univ Calif San Diego Dept Elect Engn La Jolla CA 92093 USA

State-of-the-art hypergraph partitioners follow the multilevel paradigm that constructs multiple levels of progressively coarser hypergraphs that are used to drive cut refinement on each level of the hierarchy. Multilevel partitioners are subject to two limitations: 1) hypergraph coarsening processes rely on local neighborhood structure without fully considering the global structure of the hypergraph and 2) refinement heuristics risk entrapment in local minima. In this article, we describe K-SpecPart, a supervised spectral framework for multiway partitioning that directly tackles these two limitations. K-SpecPart relies on the computation of generalized eigenvectors and supervised dimensionality reduction techniques to generate vertex embeddings. These are computational primitives that are not only fast, but embeddings also capture global structural properties of the hypergraph that are not explicitly considered by existing partitioners. K-SpecPart then converts the vertex embeddings into multiple partitioning solutions. Unlike multilevel partitioners that only consider the best solution, K-SpecPart introduces the idea of "ensembling" multiple solutions via a cut-overlay clustering technique that often enables the use of computationally demanding partitioning methods such as integer linear programming (ILP). Using the output of a standard partitioner as a supervision hint, K-SpecPart effectively combines the strengths of established multilevel partitioning techniques with the benefits of spectral graph theory and other combinatorial algorithms. K-SpecPart significantly extends ideas and algorithms that first appeared in our previous work on the bipartitioner SpecPart (Bustany et al., ICCAD 2022). Our experiments demonstrate the effectiveness of K-SpecPart. For bipartitioning, K-SpecPart produces solutions with up to similar to 15% cutsize improvement over SpecPart. For multiway partitioning, K-SpecPart produces solutions with up to similar to 20% cutsize improvement

关键词： Hypergraph partitioning partitioning algorithms physical design (EDA) spectral partitioning

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Cartesian partitioning Models for 2D and 3D Parallel SpGEMM algorithms

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2020年第12期31卷 2763-2775页

作者： Demirci, Gunduz Vehbi Aykanat, Cevdet Bilkent Univ Dept Comp Engn TR-06800 Ankara Turkey

The focus is distributed-memory parallelization of sparse-general-matrix-multiplication (SpGEMM). Parallel SpGEMM algorithms are classified under one-dimensional (1D), 2D, and 3D categories denoting the number of dimensions by which the 3D sparse workcube representing the iteration space of SpGEMM is partitioned. Recently proposed successful 2D- and 3D-parallel SpGEMM algorithms benefit from upper bounds on communication overheads enforced by 2D and 3D cartesian partitioning of the workcube on 2D and 3D virtual processor grids, respectively. However, these methods are based on random cartesian partitioning and do not utilize sparsity patterns of SpGEMM instances for reducing the communication overheads. We propose hypergraph models for 2D and 3D cartesian partitioning of the workcube for further reducing the communication overheads of these 2D- and 3D- parallel SpGEMM algorithms. The proposed models utilize two- and three-phase partitioning that exploit multi-constraint hypergraph partitioning formulations. Extensive experimentation performed on 20 SpGEMM instances by using upto 900 processors demonstrate that proposed partitioning models significantly improve the scalability of 2D and 3D algorithms. For example, in 2D-parallel SpGEMM algorithm on 900 processors, the proposed partitioning model respectively achieves 85 and 42 percent decrease in total volume and total number of messages, leading to 1.63 times higher speedup compared to random partitioning, on average.

关键词： partitioning algorithms Two dimensional displays Three-dimensional displays Solid modeling Sparse matrices Computational modeling Upper bound Sparse matrix-matrix multiplication SpGEMM sparse SUMMA SpGEMM split-3D-SpGEMM hypergraph partitioning communication cost bandwidth latency

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