Adaptive Arbitration Mechanisms for Heterogeneous NoCs under Diverse Load Scenarios

Adaptive Arbitration Mechanisms for Heterogeneous NoCs under Diverse Load Scenarios | 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 Adaptive Arbitration Mechanisms for Heterogeneous NoCs under Diverse Load Scenarios Juan Fang, Yiding Li, Yuening Wang, Zekai Jin, Yiming Yan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7175740/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 19 You are reading this latest preprint version Abstract Modern heterogeneous multi-core chips are increasingly integrating diverse types of computing units to enhance system performance, among which CPUs and GPUs exhibit significant differences in communication patterns and resource demands. These differences pose new challenges to on-chip networks (NoCs) in task scheduling and resource allocation. Efficiently coordinating resources among various computation tasks to maintain communication performance under dynamic loads has become a key issue in system design. To address this challenge, this paper proposes three adaptive arbitration mechanisms for heterogeneous NoCs, each tailored to different load scenarios. These mechanisms are respectively designed based on task-aware time-slice allocation, port load feedback adjustment, and reinforcement learning-driven dynamic arbitration. Simulation experiments on a heterogeneous platform demonstrate that the proposed methods exhibit significant advantages across different application scenarios. Notably, under high-load conditions, the RATS mechanism reduces average network latency by 40.4% and improves CPU performance by 14.2%. Heterogeneous architectures Network-on-Chip Arbitration Reinforcement Learning Dynamic Resource Scheduling 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 23 Dec, 2025 Reviews received at journal 22 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers agreed at journal 15 Dec, 2025 Reviewers agreed at journal 13 Dec, 2025 Reviewers agreed at journal 12 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 10 Dec, 2025 Reviewers agreed at journal 10 Dec, 2025 Reviewers invited by journal 10 Dec, 2025 Editor assigned by journal 23 Jul, 2025 Submission checks completed at journal 23 Jul, 2025 First submitted to journal 21 Jul, 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-7175740","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":560818676,"identity":"2af9f1f1-4b34-4589-8bce-52458c102134","order_by":0,"name":"Juan 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