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

2026-08-26

AI Summary

本文提出SAMpLE框架，通过标准化接口将机器学习模型集成到SystemC-AMS虚拟原型中，解决了现有方法的复用性和可比性问题。

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.

Problem

• Machine Learning
• Virtual Prototyping
• SystemC-AMS
• Reusability
• Reproducibility

Innovation

• Machine Learning
• SystemC-AMS
• Virtual Prototyping
• ONNX
• Timed Dataflow