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Enabling Efficient Sparse Multiplications on GPUs With Heuristic Adaptability

IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS 2025年第6期44卷 2226-2239页

作者： Xu, Jiaming Huang, Shan Li, Jinhao Huang, Guyue Xie, Yuan Wang, Yu Dai, Guohao Shanghai Jiao Tong Univ Qing Yuan Res Inst Shanghai 200240 Peoples R China Univ Calif Santa Barbara Dept Elect & Comp Engn Santa Barbara CA 93106 USA Tsinghua Univ Dept Elect Engn Beijing 100190 Peoples R China Hong Kong Univ Sci & Technol Dept Elect & Comp Engn Hong Kong Peoples R China

Sparse matrix-vector/matrix multiplication, namely SpMMul, has become a fundamental operation during model inference in various domains. Previous studies have explored numerous optimizations to accelerate it. However, to enable efficient end-to-end inference, the following challenges remain unsolved: 1) incomplete design space and time-consuming preprocessing. Previous methods optimize SpMMul in limited loops and neglect the potential space exploration for further optimization, resulting in >30% waste of computing power. In addition, the preprocessing overhead in SparseTIR and DTC-SpMM is 1000x larger than sparse computing;2) incompatibility between static dataflow and dynamic input. A static dataflow can not always be efficient to all input, leading to >80% performance loss;and 3) simplistic algorithm performance analysis. Previous studies primarily analyze performance from algorithmic advantages, without considering other aspects like hardware and data features. To tackle the above challenges, we present DA-SpMMul, a Data-Aware heuristic GPU implementation for SpMMul in multiplatforms. DA-SpMMul creatively proposes: 1) complete design space based on theoretical computations and nontrivial implementations without preprocessing. We propose three orthogonal design principles based on theoretical computations and provide nontrivial implementations on standard formats, eliminating the complex preprocessing;2) feature-enabled adaptive algorithm selection mechanism. We design a heuristic model to enable algorithm selection considering various features;and 3) comprehensive algorithm performance analysis. We extract the features from multiple perspectives and present a comprehensive performance analysis of all algorithms. DA-SpMMul supports PyTorch on both NVIDIA and AMD and achieves an average speedup of 3.33x and 3.02x over NVIDIA cuSPARSE, and 12.05x and 8.32x over AMD rocSPARSE for sparse matrix-vector multiplication and sparse matrix-matrix multiplication, and up to 1

关键词： Sparse matrices Heuristic algorithms Optimization Hardware Kernel Instruction sets inference algorithms Graphics processing units Tensors Performance analysis GPU heuristic adaptability sparse matrix-matrix multiplication (SpMM) sparse matrix-vector multiplication (SpMV)

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Online Learning of Expanding Graphs

IEEE OPEN JOURNAL OF SIGNAL PROCESSING

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IEEE OPEN JOURNAL OF SIGNAL PROCESSING 2025年 6卷 247-255页

作者： Rey, Samuel Das, Bishwadeep Isufi, Elvin King Juan Carlos Univ Dept Signal Theory & Commun Madrid 28933 Spain Delft Univ Technol Intelligent Syst Dept NL-2628 CD Delft Netherlands

This paper addresses the problem of online network topology inference for expanding graphs from a stream of spatiotemporal signals. Online algorithms for dynamic graph learning are crucial in delay-sensitive applications or when changes in topology occur rapidly. While existing works focus on inferring the connectivity within a fixed set of nodes, in practice, the graph can grow as new nodes join the network. This poses additional challenges like modeling temporal dynamics involving signals and graphs of different sizes. This growth also increases the computational complexity of the learning process, which may become prohibitive. To the best of our knowledge, this is the first work to tackle this setting. We propose a general online algorithm based on projected proximal gradient descent that accounts for the increasing graph size at each iteration. Recursively updating the sample covariance matrix is a key aspect of our approach. We introduce a strategy that enables different types of updates for nodes that just joined the network and for previously existing nodes. To provide further insights into the proposed method, we specialize it in Gaussian Markov random field settings, where we analyze the computational complexity and characterize the dynamic cumulative regret. Finally, we demonstrate the effectiveness of the proposed approach using both controlled experiments and real-world datasets from epidemic and financial networks.

关键词： Topology Network topology Heuristic algorithms Signal processing algorithms Signal processing Covariance matrices Mathematical models inference algorithms Computational complexity Vectors Topology inference dynamic graph learning online graph learning expanding graphs graph signal processing

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inference of Aggregate Hidden Markov Models With Continuous Observations

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IEEE CONTROL SYSTEMS LETTERS 2022年 6卷 2377-2382页

作者： Zhang, Qinsheng Singh, Rahul Chen, Yongxin Georgia Inst Technol Sch Aerosp Engn Atlanta GA 30332 USA

We consider a class of inference problems for large populations where each individual is modeled by the same hidden Markov model (HMM). We focus on aggregate inference problems in HMMs with discrete state space and continuous observation space. The continuous observations are aggregated in a way such that the individuals are indistinguishable from measurements. We propose an aggregate inference algorithm called continuous observation collective forward-backward algorithm. It extends the recently proposed collective forward-backward algorithm for aggregate inference in HMMs with discrete observations to the case of continuous observations.

关键词： Hidden Markov models Aggregates inference algorithms Statistics Sociology Noise measurement Markov processes Markov process filtering stochastic systems

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Joint Impulsive Noise Mitigation and Sparse Channel Estimation for Mobile Underwater Acoustic Communication

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IEEE WIRELESS COMMUNICATIONS LETTERS 2024年第8期13卷 2095-2099页

作者： Yu, Hua Liang, Yaokun Ma, Haonan Ji, Fei Xu, Lijun South China Univ Technol Schoolof Elect & Informat Engn Guangzhou 510640 Peoples R China Minist Nat Resources Key Lab Marine Environm Survey Technol & Applicat Guangzhou 510300 Peoples R China Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China

This letter presents a joint impulsive noise mitigation and sparse channel estimation algorithm for single-carrier systems in mobile underwater acoustic communication (UAC) to address the challenges posed by impulsive noise interference. The basis expansion model (BEM) is introduced to describe the underwater acoustic time-varying channels and reduce the channel parameters to be estimated. Based on the model, the variational Bayesian inference algorithm is employed to derive an iterative estimation expression for jointly estimating the sparse BEM channel coefficients and the impulsive noise. The proposed algorithm mitigates the impact of impulsive noise, leading to enhanced communication reliability. Simulations and sea trials validate the effectiveness of the proposed algorithm, highlighting its potential for practical applications in mobile underwater acoustic communication systems.

关键词： Noise Channel estimation Symbols inference algorithms Underwater acoustics Estimation Bayes methods Underwater acoustic communication impulsive noise basic expansion model sparse Bayesian learning channel estimation

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Population Monte Carlo With Normalizing Flow

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IEEE SIGNAL PROCESSING LETTERS 2024年 31卷 16-20页

作者： Pal, Soumyasundar Valkanas, Antonios Coates, Mark Huawei Noahs Arc Lab Montreal PQ H3A 2A7 Canada McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 2A7 Canada

Adaptive importance sampling (AIS) methods provide a useful alternative to Markov Chain Monte Carlo (MCMC) algorithms for performing inference of intractable distributions. Population Monte Carlo (PMC) algorithms constitute a family of AIS approaches which adapt the proposal distributions iteratively to improve the approximation of the target distribution. Recent work in this area primarily focuses on ameliorating the proposal adaptation procedure for high-dimensional applications. However, most of the AIS algorithms use simple proposal distributions for sampling, which might be inadequate in exploring target distributions with intricate geometries. In this work, we construct expressive proposal distributions in the AIS framework using normalizing flow, an appealing approach for modeling complex distributions. We use an iterative parameter update rule to enhance the approximation of the target distribution. Numerical experiments show that in high-dimensional settings, the proposed algorithm offers significantly improved performance compared to the existing techniques.

关键词： Proposals Signal processing algorithms Monte Carlo methods Artificial intelligence inference algorithms Approximation algorithms Sociology Bayesian inference importance sampling normalizing flow population Monte Carlo

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Completing and Predicting Internet Traffic Matrices Using Adversarial Autoencoders and Hidden Markov Models

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第3期20卷 2244-2258页

作者： Sacco, Alessio Esposito, Flavio Marchetto, Guido Politecn Torino DAUIN I-10129 Turin Italy St Louis Univ Dept Comp Sci St Louis MO 63103 USA

Internet traffic matrices are used nowadays for a variety of network management operations, from planning to repairing. Despite years of research on the topic, obtaining a global view of traffic is still challenging and error-prone. Due to flaws in the measurement systems and possible failure in data collection tools, missing values are unavoidable. It is thus helpful for many network operators to recover the missing data from the partial direct measurements. While some existing matrix completion methods allowed this reconstruction, they do not fully consider network traffic behavior and hidden traffic characteristics, showing the inability to adapt to multiple scenarios. Others instead make assumptions about the matrix structure that may be invalid or impractical, curtailing the applicability. In this paper, we propose Hide & Seek, a novel matrix completion and prediction algorithm based on a combination of generative autoencoders and Hidden Markov Models. After an extensive experimental evaluation based on both real-world datasets and on a testbed, we demonstrated how our algorithm can accurately reconstruct missing values while also predicting their short-term evolution.

关键词： Hidden Markov models Data models Predictive models inference algorithms Estimation Prediction algorithms Time measurement Traffic matrix machine learning inference

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PolyVerif: An Open-Source Environment for Autonomous Vehicle Validation and Verification Research Acceleration

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IEEE ACCESS 2023年 11卷 28343-28354页

作者： Razdan, Rahul Akbas, Mustafa Ilhan Sell, Raivo Bellone, Mauro Menase, Mahesh Malayjerdi, Mohsen Florida Polytech Univ Lakeland FL 33805 USA Embry Riddle Aeronaut Univ Dept Elect Engn & Comp Sci Daytona Beach FL 32114 USA Tallinn Univ Technol Dept Mech & Ind Engn EE-19086 Tallinn Estonia Tallinn Univ Technol FinEst Smart City Ctr Excellence EE-19086 Tallinn Estonia Acclivis Technol Pvt Ltd Pune 411038 Maharashtra India

Validation and Verification (V&V) of Artificial Intelligence (AI) based cyber physical systems such as Autonomous Vehicles (AVs) is currently a vexing and unsolved problem. AVs integrate subsystems in areas such as detection, sensor fusion, localization, perception, and path planning. Each of these subsystems contains significant AI content integrated with traditional hardware and software components. The complexity for validating even a subsystem is daunting and the task of validating the whole system is nearly impossible. Fundamental research in advancing the state-of-the-art for AV V&V is required. However, for V&V researchers, it is exceedingly difficult to make progress because of the massive infrastructure requirements to demonstrate the viability of any solution. This paper presents PolyVerif, the world's first open-source solution focused on V&V researchers with the objective of accelerating the state-of-the-art for AV V&V research. PolyVerif provides an AI design and verification framework consisting of a digital twin creation process, an open-source AV engine, access to several open-source physics based simulators, and open-source symbolic test generation engines. PolyVerif's objective is to arm V&V researchers with a framework which extends the state-of-the-art on any one of the many major axes of interest and use the remainder of the infrastructure to quickly demonstrate the viability of their solution. Given its open-source nature, researchers can also contribute their innovations to the project. Using this critical property of open-source environments, the innovation rate of the whole research community to solve these vexing issues can be greatly accelerated. Finally, the paper also presents results from several projects which have used PolyVerif.

关键词： Software engineering Artificial intelligence Software algorithms inference algorithms Autonomous vehicles Hardware System analysis and design validation and verification modeling and simulation artificial intelligence

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Fuzzy Clustering and Routing Protocol With Rules Tuned by Improved Particle Swarm Optimization for Wireless Sensor Networks

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IEEE ACCESS 2023年 11卷 128784-128800页

作者： Liu, Yuebo Yu, Haitao Li, Hongyan Liu, Qingxue Jilin Univ Architecture & Technol Coll Comp Engn & Artificial Intelligence Changchun 130114 Peoples R China Jilin Commun Polytech Dept Informat Ctr Changchun 130015 Peoples R China Changchun Informat Technol Coll Sch Management Changchun 131103 Peoples R China

Fuzzy clustering and routing protocols have been proven to improve energy efficiency, extend network scalability, increase network throughput, balance network load as well as prolong network lifetime. However, rules defined manually according to field experts are impossible or impractical to achieve the optimal solution for a Fuzzy inference System (FIS). Therefore, a Novel Fuzzy Clustering and Routing Protocol called NFCRP is proposed in this paper by using an improved Particle Swarm Optimization (PSO) algorithm to tune the fuzzy rules. Firstly, one FIS is used to complete clustering based on effective input parameters including residual energy, node degree deviation, and distance to centrality, thereby forming optimal clusters and minimizing the intra-cluster energy consumption. Secondly, the other FIS is adopted to perform routing with descriptors residual energy, distance to BS, and data load deviation, hence addressing the inter-cluster energy consumption. Finally, the rules of both FISs are tuned by an improved PSO algorithm whose parameters are updated by introducing chaotic mapping and adaptive inertia weight. Simulation experiments were conducted to verify the performance of NFCRP against LEACH, EFUCA, EEFUC, FBCR and FMSFLA. According to the results, the average network lifetime of NFCRP increased by 79.59%, 47.99%, 50.35%,15.66 and 13.04%, compared to LEACH, EEFUC, EFUCA, FBCR and FMSFLA. For the average standard deviation of CH's traffic load, NFCRP decreased it by 29.29% over EEFUC, 31.42% over EFUCA, and 25.28% over FMSFLA. For network throughput, NFCRP outperformed LEACH, EEFUC, EFUCA, FBCR and FMSFLA by 16.87%, 46.52%, 48.18%, 29.97 and 71.79%. In addition, NFCRP also reduced energy consumption by 53.95%, 23.76%, 38.72%, 15.71 and 27.18% as compared to LEACH, EEFUC, EFUCA, FBCR and FMSFLA, respectively.

关键词： Routing protocols Clustering algorithms Optimization Spread spectrum communication Relays Particle swarm optimization inference algorithms Fuzzy logic Clustering and routing fuzzy inference systems particle swarm optimization energy balance wireless sensor networks

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An Automated Method to Correct Artifacts in Neural Text-to-Speech Models

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2025年 33卷 1375-1388页

作者： Jeong, Seongyeop Sung, June Sig Hwang, Inchul Choi, Jaesik Korea Adv Inst Sci & Technol Kim Jaechul Grad Sch Artificial Intelligence Daejeon 34141 South Korea Ineeji Seongnam 13558 South Korea Samsung Elect Mobile EXperience Business Suwon 16677 South Korea

Recent advances in deep learning technology have enabled high-quality speech synthesis, and text-to-speech models are widely used in a variety of applications. However, even state-of-the-art models still produce artificial speech, highlighting the need to correct errors in synthesized speech. Traditional speech correction methodologies face a number of challenges, such as the inefficiency of manually specifying errors, the need to retrain models, and the need for additional data to correct synthesized speech errors. In this paper, we present a novel approach that detects and corrects contextual errors within a model to improve synthetic speech without requiring additional resources or model retraining. Specifically, we analyze the inherent limitations of neural network encoders responsible for contextualizing input sentences and propose a method that automatically identifies abnormal encoder context vectors. We also introduce a correction algorithm that enhances the quality of speech by correcting the incorrect relationships between phonemes that cause abnormal encoder context vectors. Our algorithm demonstrated a 25.86% and 4.69% reduction in alignment errors and a 2.25% and 0.63% improvement in objective metrics such as Fr & eacute;chet Wav2Vec distance for the Tacotron2 and VITS models. In addition, our algorithm improved the comparative mean opinion scores, a subjective evaluation, by 1.34 and 0.52. The results support the feasibility of a novel approach to identify, correct, and improve defects automatically in neural speech synthesis models. The results support the feasibility of a novel approach to identify, correct, and improve defects automatically in neural speech synthesis models.

关键词： Vectors Context modeling Data models Training Analytical models Neurons Speech enhancement inference algorithms Biological neural networks Text to speech Contextualization error interpreting neural networks speech artifacts speech correction text-to-speech

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Bayesian Tensor Tucker Completion With a Flexible Core

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2023年 71卷 4077-4091页

作者： Tong, Xueke Cheng, Lei Wu, Yik-Chung Univ Hong Kong Dept Elect & Elect Engn Hong Kong 999077 Peoples R China Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China

Tensor completion is a vital task in multi-dimensional signal processing and machine learning. To recover the missing data in a tensor, various low-rank structures of a tensor can be assumed, and Tucker format is a popular choice. However, the promising capability of Tucker completion is realized only when we can determine a suitable multilinear rank, which controls the model complexity and thus is essential to avoid overfitting/underfitting. Rather than exhaustively searching the best multilinear rank, which is computationally inefficient, recent advances have proposed a Bayesian way to learn the multilinear rank from training data automatically. However, in prior arts, only a single parameter is dedicated to learn the variance of the core tensor elements. This rigid assumption restricts the modeling capabilities of existing methods in real-world data, where the core tensor elements may have a wide range of variances. To have a flexible core tensor while still retaining succinct Bayesian modeling, we first bridge the tensor Tucker decomposition to the canonical polyadic decomposition (CPD) with low-rank factor matrices, and then propose a novel Bayesian modeling based on the Gaussian-inverse Wishart prior. inference algorithm is further derived under the variational inference framework. Extensive numerical studies on synthetic data and real-world datasets demonstrate the significantly improved performance of the proposed algorithm in terms of multilinear rank learning and missing data recovery.

关键词： Tensors Bayes methods Data models Matrix decomposition inference algorithms Computational modeling Predictive models Tensor decomposition Bayesian Tucker model multilinear rank estimation Gaussian-Wishart priors

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