• Home Systems & Design Low Power - High Performance Manufacturing, Packaging & Materials Test, Measurement & Analytics Auto, Security & Enabling Technologies Special Reports Business & Startups Jobs Knowledge Center Technical PapersHome’;AI/ML/DLArchitecturesAutomotive/ AerospaceCommunication/Data MovementDesign & VerificationLithographyManufacturingMaterialsMemoryOptoelectronics / PhotonicsPackagingPower & PerformanceQuantumSecurityTest, Measurement, Analytics tech papersTransistorsZ-End Applications Home AI/ML/DL Architectures Automotive/ Aerospace Communication/Data Movement Design & Verification Lithography Manufacturing Materials Memory Optoelectronics / Photonics Packaging Power & Performance Quantum Security Test, Measurement, Analytics tech papers Transistors Z-End Applications Events & WebinarsEventsWebinars Events Webinars Videos & ResearchVideosIndustry Research Videos Industry Research Newsletters & StoreNewslettersStore Newsletters Store MENUHomeSpecial ReportsSystems & DesignLow Power-High PerformanceManufacturing, Packaging & MaterialsTest, Measurement & AnalyticsAuto, Security & Enabling TechnologiesKnowledge CenterVideosStartup CornerBusiness & StartupsJobsTechnical PapersEventsWebinarsIndustry ResearchNewslettersStoreSpecial Reports Home Special Reports Systems & Design Low Power-High Performance Manufacturing, Packaging & Materials Test, Measurement & Analytics Auto, Security & Enabling Technologies Knowledge Center Videos Startup Corner Business & Startups Jobs Technical Papers Events Webinars Industry Research Newsletters Store Special Reports Automated MLIR-based HLS framework That Generates FPGA HW Designs From A Variety of CNN Layers (TU Dresden) TU Dresdenresearchers published “MING: An Automated CNN-to-Edge MLIR HLS framework.” Abstract “Driven by the increasing demand for low-latency and real-time processing, machine learning applications are steadily migrating toward edge computing platforms, where Field-Programmable Gate Arrays (FPGAs) ar

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  • TU Dresden researchers published “MING: An Automated CNN-to-Edge MLIR HLS framework.” Abstract “Driven by the increasing demand for low-latency and real-time processing, machine learning applications are steadily migrating toward edge computing platforms, where Field-Programmable Gate Arrays (FPGAs) are widely adopted for their energy efficiency compared to CPUs and GPUs. To generate high-performance and low-power FPGA designs, several frameworks built upon High Level Synthesis (HLS) vendor tools have been proposed, among which MLIR-based frameworks are gaining significant traction due to thei

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