A Dynamic Traffic-Aware VC Partitioning Strategy and Optimization in CPU-GPU Heterogeneous Network-on-Chip

A Dynamic Traffic-Aware VC Partitioning Strategy and Optimization in CPU-GPU Heterogeneous Network-on-Chip | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Dynamic Traffic-Aware VC Partitioning Strategy and Optimization in CPU-GPU Heterogeneous Network-on-Chip Juan Fang, Yiming Yan, Haoyu Cheng, Yuening Wang, Juncheng Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6909730/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 20 You are reading this latest preprint version Abstract With the growing demand for heterogeneous computing in high-performance applications, there is intense competition between CPU-GPU heterogeneous processors based on on-chip network communication. However, existing communication resources are insufficient to meet the increasing bandwidth demands, which can lead to traffic congestion and ultimately degrade system performance. To address the traffic congestion problem in data transmission, we first analyze traffic characteristics and reveal the load imbalance between request traffic and reply traffic. Based on this observation, we propose a Traffic-Type-aware Virtual Channel Partitioning (T-VCP) strategy. In addition, we introduce a Port Congestion-Aware Routing (PCAR) algorithm, which alleviates local hotspots by dynamically selecting paths according to real-time congestion monitoring. Furthermore , to adapt to runtime traffic fluctuations, we present a Dynamic Virtual Channel Partitioning (DT-VCP) strategy. Experimental results show that, in a 4×4 CPU-GPU heterogeneous NoC, the T-VCP+PCAR strategy reduces network latency by 18.3% and improves IPC by 3.4% compared to traditional XY routing without VC partitioning. The DT-VCP+PCAR strategy further reduces latency by 22.8% and improves IPC by 9.4%. Additional experiments on a larger 8×8 architecture demonstrate a 26.5% latency reduction, confirming the good scalability of the proposed method. Heterogeneous Architectures Network-on-Chip (NoC) Virtual Channel Partitioning Routing Algorithm Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 27 Dec, 2025 Reviews received at journal 26 Dec, 2025 Reviews received at journal 26 Dec, 2025 Reviews received at journal 24 Dec, 2025 Reviews received at journal 22 Dec, 2025 Reviews received at journal 21 Dec, 2025 Reviews received at journal 11 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers agreed at journal 09 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers invited by journal 08 Dec, 2025 Editor assigned by journal 21 Jun, 2025 Submission checks completed at journal 21 Jun, 2025 First submitted to journal 16 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6909730","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":558443972,"identity":"28f1a03c-d344-4313-95dd-d81cf428c1a1","order_by":0,"name":"Juan Fang","email":"","orcid":"","institution":"Beijing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Fang","suffix":""},{"id":558443975,"identity":"09e0b6b3-986f-4d2e-af6f-890593c60e88","order_by":1,"name":"Yiming Yan","email":"","orcid":"","institution":"Beijing University of 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