ACM Computing Surveys最新文献_第10页

Survey of GPU Acceleration in Acoustics and Audio Signal Processing GPU加速在声学和音频信号处理中的研究进展

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-07-13 DOI: 10.1145/3830900

André Carvalho, Aleksandar Ilic, Leonel Sousa

{"title":"Survey of GPU Acceleration in Acoustics and Audio Signal Processing","authors":"André Carvalho, Aleksandar Ilic, Leonel Sousa","doi":"10.1145/3830900","DOIUrl":"https://doi.org/10.1145/3830900","url":null,"abstract":"Acoustics and audio signal processing play a substantial role in our daily lives. Multimedia applications and valuable audio tools rely on this field for technological advancements. On the other hand, their efficiency depends on the capabilities of computing systems that provide the required computing resources. GPUs (Graphics Processing Units) were originally designed for graphics rendering, but they have transformed various fields by accelerating computationally intensive tasks with their parallel processing architecture. This survey explores the potential of GPUs in accelerating acoustics and audio signal processing tasks. A comprehensive overview is provided, and it is found that audio filtering, room acoustics, and sound synthesis are the foremost research fields benefiting from this massively parallel processing. Opportunities have been identified for further research in unexplored domains and established areas that require updates due to improved algorithms and new hardware technology. This survey can be useful for anyone looking for a background on the subject and wanting to understand which tools and applications have been explored and tested, and potential future directions.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"32 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148428898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extending Silicon Lifetime: A Review of Design Techniques for Reliable Integrated Circuits 延长硅寿命：可靠集成电路设计技术综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-07-10 DOI: 10.1145/3829075

Shaik Babu, Fan Hu, Linyu Zhu, Sonal Singhal, Xinfei Guo

{"title":"Extending Silicon Lifetime: A Review of Design Techniques for Reliable Integrated Circuits","authors":"Shaik Babu, Fan Hu, Linyu Zhu, Sonal Singhal, Xinfei Guo","doi":"10.1145/3829075","DOIUrl":"https://doi.org/10.1145/3829075","url":null,"abstract":"Reliability is an increasing concern in modern computing. Integrated circuits (ICs) form the backbone of computing devices across industries, including AI, consumer electronics, healthcare, automotive, industrial, and aerospace. While Moore’s Law has driven ICs toward smaller dimensions, higher performance, and greater energy efficiency, transistor scaling to atomic levels has exacerbated aging-related degradation mechanisms such as Bias Temperature Instability (BTI), Hot Carrier Injection (HCI), Time-Dependent Dielectric Breakdown (TDDB), Electromigration (EM), and stochastic aging-induced variations. Applications like AI training and autonomous driving demand continuous, reliable operation to minimize recovery costs and enhance safety. Additionally, the high cost of chip replacement underscores the need for extended lifespans. These challenges highlight the urgency of designing more reliable ICs as critical computing infrastructure. This survey explores aging in ICs, covering key degradation mechanisms and mitigation strategies. It provides a comprehensive overview of aging’s impact, key monitoring techniques at circuit and system levels, and circuit-level mitigation strategies tailored for digital, analog, and SRAM circuits. The survey also examines emerging software-driven approaches in design automation and aging characterization, which are transforming traditional reliability optimization. Finally, it discusses challenges and future directions to improve aging management and ensure long-term IC reliability across applications.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"17 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148414793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research 深入研究推荐系统中的公平、偏见、威胁和隐私：见解和未来研究

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-07-10 DOI: 10.1145/3821405

Falguni Roy, Xiaofeng Ding, Kim-Kwang Raymond Choo

{"title":"A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research","authors":"Falguni Roy, Xiaofeng Ding, Kim-Kwang Raymond Choo","doi":"10.1145/3821405","DOIUrl":"https://doi.org/10.1145/3821405","url":null,"abstract":"Recommender systems personalize digital experiences across e-commerce, streaming, and social media, yet they face linked challenges in fairness, bias, threats, and privacy. Bias can lead to unequal treatment, so fairness seeks equitable recommendations for all stakeholders. At the same time, adversarial attacks can erode reliability, and extensive use of personal data creates privacy risks that demand robust safeguards. This study surveys these challenges, shows how algorithmic choices can unintentionally reinforce disparities, and reviews strategies for fair recommendation and privacy-preserving learning. We frame these aspects with a unified Privacy–Accuracy–Fairness plus Threat framework that spans data retrieval, ranking and serving, and we advocate standardized evaluation on fixed candidate item pools to isolate tradeoffs between quality and responsibility metrics. By integrating these dimensions, we synthesize current limitations and outline future directions to improve threat resilience, fairness and privacy. We also situate the discussion in ethical and regulatory contexts and translate those principles into practical controls that foster trustworthy systems for diverse populations.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"273 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148414792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative AI at the Edge: A Comprehensive Survey of Architectures, Hardware and Applications 边缘的生成人工智能：架构、硬件和应用的全面调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-07-07 DOI: 10.1145/3829079

Mozhgan Navardi, Yuzhe Fu, Yueqian Lin, Hai Li, Yiran Chen, Tinoosh Mohsenin

{"title":"Generative AI at the Edge: A Comprehensive Survey of Architectures, Hardware and Applications","authors":"Mozhgan Navardi, Yuzhe Fu, Yueqian Lin, Hai Li, Yiran Chen, Tinoosh Mohsenin","doi":"10.1145/3829079","DOIUrl":"https://doi.org/10.1145/3829079","url":null,"abstract":"Generative Artificial Intelligence (GenAI) leverages generative models, including Diffusion models and Foundation Models (FMs) such as Large Language Models (LLMs), to generate new data. GenAI has attracted growing attention as it enables applications such as text generation, image synthesis, and multimodal reasoning. However, deploying such GenAI models on resource-constrained edge devices poses key challenges. Edge devices typically have limited computational power and strict energy budgets. They also face the memory-wall problem, where the cost of moving data between memory and compute units exceeds the cost of computation itself. This survey provides a comprehensive overview of techniques that enable GenAI deployment at the edge, covering software optimizations, hardware innovations, and system-level frameworks. While all three categories are discussed, particular emphasis is given to hardware-focused approaches. We review recent hardware studies and classify them into three main levels: computation, memory, and scheduling, with eight finer-grained subcategories that capture the mainstream hardware techniques. Many of these are hardware–software co-design strategies that align GenAI workloads with edge resource constraints. By synthesizing state-of-the-art solutions and identifying open challenges, this survey outlines a roadmap for building efficient GenAI systems at the edge.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"22 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148401512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Generative Artificial Intelligence for Internet of Things Computing: A Systematic Survey 物联网计算的生成式人工智能：系统综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-07-07 DOI: 10.1145/3830013

Fabrizio Mangione, Claudio Savaglio, Giancarlo Fortino

{"title":"Generative Artificial Intelligence for Internet of Things Computing: A Systematic Survey","authors":"Fabrizio Mangione, Claudio Savaglio, Giancarlo Fortino","doi":"10.1145/3830013","DOIUrl":"https://doi.org/10.1145/3830013","url":null,"abstract":"The integration of Generative Artificial Intelligence (GenAI) and the Internet of Things (IoT) is attracting significant attention due to their rapid, individual evolution and their widespread joint adoption across domains such as Healthcare, Manufacturing, and Smart Cities. Consequently, their increasing popularity has catalyzed extensive research to understand the interplay between GenAI and IoT, and how their synergy can advance the state-of-the-art in both domains. However, despite the increasing prominence of GenAI for IoT Computing, much of the existing research remains focused on specific, narrowly scoped applications. This fragmented approach highlights the need for a more comprehensive analysis of the potential, challenges, and implications of GenAI integration within the broader IoT ecosystem. The survey exactly aims to address this gap by providing a holistic overview of the opportunities and issues arising from the convergence of these mainstream paradigms. Our contribution is a systematic literature review following the PRISMA methodology: a comparison framework is presented, and well-defined research questions are outlined to comprehensively explore the current and future directions of GenAI-aided IoT Computing.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"24 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148401115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Erratum: Navigating Posit Arithmetic: A Comprehensive Survey of Principles, Hardware, and Applications 勘误：导航位置算术：原理，硬件和应用的综合调查

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-07-06 DOI: 10.1145/3820485

David Mallasén, Raul Murillo, Guillermo Botella, Alberto Antonio Del Barrio

引用次数: 0

Anomaly Detection using Knowledge Graphs: A Survey for Network Management and Cybersecurity Application 基于知识图谱的异常检测：网络管理与网络安全应用综述

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-07-03 DOI: 10.1145/3830090

Lionel Tailhardat, Raphael Troncy, Yoan Chabot

{"title":"Anomaly Detection using Knowledge Graphs: A Survey for Network Management and Cybersecurity Application","authors":"Lionel Tailhardat, Raphael Troncy, Yoan Chabot","doi":"10.1145/3830090","DOIUrl":"https://doi.org/10.1145/3830090","url":null,"abstract":"In telecommunications and computer networks, effective incident management highly depends on handling data heterogeneity and providing detailed event context. While knowledge graphs can assist with data integration and AI techniques with contextualization, these aspects are often treated separately, limiting progress toward detailed, explainable, shareable network behavior understanding. This article offers a structured overview, through three perspectives, of how integrating semantic knowledge representations and AI can address this gap. First, we analyze current Network Monitoring Systems (NMS) and Security Information and Event Management (SIEM) systems with respect to the needs of NetOps and SecOps experts. We identify key limitations and discuss enhancements through the incorporation of network topology and operational data, the use of semantic models, and the integration of multiple analytical techniques working together. Next, we review semantic models aligned with NetOps and SecOps, assessing their coverage and expressivity to inform decision support system designers about their potential for reuse, combination, and their capabilities in representing and reasoning about network and system state changes. Finally, we categorize AI techniques by approach, level of determinism, the knowledge representations used, and the incident management steps they address. We identify families of techniques and how each serves operational needs. Additionally, we highlight system design patterns that could maximize, either within families of techniques or through their combination, a detailed understanding of the interplay between network architecture and operational dynamics. We synthesize these three perspectives into a high-level design proposal for a next-generation NMS/SIEM combining logic-based and probabilistic reasoning within a semantic ecosystem, aiming to further automate context-aware incident management in complex Information and Communications Technology (ICT) environments.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"226 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148401117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Neural Networks Inspired by Differential Equations 受微分方程启发的深度神经网络

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-07-01 DOI: 10.1145/3828753

Yongshuai Liu, Lianfang Wang, Kuilin Qin, Qinghua Zhang, Faqiang Wang, Li Cui, Jun Liu, Yuping Duan, Tieyong Zeng

{"title":"Deep Neural Networks Inspired by Differential Equations","authors":"Yongshuai Liu, Lianfang Wang, Kuilin Qin, Qinghua Zhang, Faqiang Wang, Li Cui, Jun Liu, Yuping Duan, Tieyong Zeng","doi":"10.1145/3828753","DOIUrl":"https://doi.org/10.1145/3828753","url":null,"abstract":"Deep learning has emerged as an important technology in fields such as computer vision, scientific computing, and dynamical systems, thereby driving major advancements across these domains. However, neural networks persistently face challenges related to theoretical understanding, interpretability, and generalization. Thus, researchers are increasingly adopting a differential equations perspective to propose a unified theoretical framework and systematic design methodologies. We establish a comprehensive taxonomy of the literature from two distinct perspectives. First, we classify the models based on their mathematical formulations, covering ordinary, partial, and stochastic differential equations (ODEs, PDEs, and SDEs). Second, we categorize them by their methodological roles, distinguishing between equation-guided networks and networks designed for solving equations. Moreover, we conduct a qualitative meta-analysis supported by consolidated evidence summaries from existing literature to illustrate their characteristics and performance. Finally, we explore promising research directions in integrating differential equations with deep learning to offer new insights for developing computational methods with improved interpretability and generalization.","PeriodicalId":50926,"journal":{"name":"ACM Computing Surveys","volume":"364 1","pages":""},"PeriodicalIF":16.6,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148371844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On Replacing Cryptopuzzles with Useful Computation in Blockchain Proof-of-Work Protocols 区块链工作量证明协议中用有用的计算替换密码谜题

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-06-29 DOI: 10.1145/3828717

Andrea Merlina, Thiago Garrett, Roman Vitenberg

引用次数: 0

A Comprehensive Survey of Deep Learning for Entity Resolution 面向实体解析的深度学习综合研究

IF 16.6 1区计算机科学

ACM Computing Surveys Pub Date : 2026-06-27 DOI: 10.1145/3828660

Dimitrios Karapiperis, Christos Tjortjis, Vassilios Verykios

引用次数: 0