aiqbench analysis
This article introduces SAMpLE, an open-source framework that standardizes the integration of machine learning models into SystemC-AMS virtual prototyping environments. By treating ML components as first-class Timed Dataflow elements with ONNX support, it addresses critical reproducibility and reuse challenges in embedded system simulation workflows. The tool enables seamless evaluation of diverse ML solutions without requiring manual code re-implementation for each new model.
SAMpLE: A SystemC-AMS Machine LEarning-based Framework for Virtual Prototyping
arXiv:2608.25910v1 Announce Type: cross 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 done through ad hoc solutions, which limits reuse, comparability, and reproducibility. This paper presents \textbf{\textit{SAMpLE}}, an open-source SystemC-AMS-based framework that integrates ML models as first-class Timed Dataflow (TDF) components through a standardized plug-and-play interface. SAMpLE provides two execution backends: a native C++ backend for online training of lightweight models, and an offline backend for executing externally developed models without requiring re-implementation in C++ or manual integration steps. The framework uses ONNX as a standard model exchange format to enable integration of externally trained ML models into SystemC-AMS simulations, and allows the evaluation of different ML-based solutions within the same testbench, dataset, and simulation workflow. The modular design and unified and reproducible environment will allow future extensions of SAMpLE to new models, without modifying the SystemC-AMS structure.
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 done through ad hoc solutions, which limits reuse, comparability, and reproducibility. This paper presents \textbf{\textit{SAMpLE}}, an open-source SystemC-AMS-based framework that integrates ML models as first-class Timed Dataflow (TDF) components through a standardized plug-and-play interface. SAMpLE provides two execution backends: a native C++ backend for online training of lightweight models, and an offline backend for executing externally developed models without requiring re-implementation in C++ or manual integration steps. The framework uses ONNX as a standard model exchange format to enable integration of externally trained ML models into SystemC-AMS simulations, and allows the evaluation of different ML-based solutions within the same testbench, dataset, and simulation workflow. The modular design and unified and reproducible environment will allow future extensions of SAMpLE to new models, without modifying the SystemC-AMS structure.
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2608.25910 [cs.CL]
(or arXiv:2608.25910v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2608.25910 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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