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New SAMpLE framework integrates ML models into SystemC-AMS virtual prototypes

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SAMpLE, an open-source framework that integrates machine learning models into SystemC-AMS virtual prototypes. This framework allows ML models to function as first-class Timed Dataflow components, simplifying their incorporation into simulations. SAMpLE offers two execution backends: one for online training of lightweight models in C++ and another for executing externally developed models without manual integration. It utilizes ONNX for model exchange, enabling the evaluation of various ML-based solutions within a unified and reproducible simulation environment. AI

IMPACT This framework could streamline the integration of AI models into hardware design simulations, potentially accelerating development cycles.

RANK_REASON The cluster describes a new research paper detailing a framework for integrating machine learning into virtual prototyping. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SAMpLE framework integrates ML models into SystemC-AMS virtual prototypes

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The cluster describes a new research paper detailing a framework for integrating machine learning into virtual prototyping. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Andrei Mihai Albu, Sara Vinco ·

    SAMpLE: A SystemC-AMS Machine LEarning-based Framework for Virtual Prototyping

    arXiv:2608.25910v1 Announce Type: new Abstract: Machine Learning (ML) is increasingly used in virtual prototypes of embedded systems to model behaviors that are difficult to capture analytically. However, integrating ML models into virtual platform simulation is still typically d…

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