发表机构
Politecnico di Torino（都灵理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出开源SAMpLE框架，基于SystemC-AMS集成ML模型为TDF组件，通过ONNX格式实现模型交互，支持两种执行后端，可在同一流程中评估不同ML方案，便于扩展。
AI 中文摘要
机器学习（ML）越来越多地被用于嵌入式系统的虚拟原型中，以建模难以通过分析方法捕获的行为。然而，将ML模型集成到虚拟平台仿真中通常仍通过临时解决方案完成，这限制了复用性、可比性和可复现性。本文提出了SAMpLE，这是一个开源的基于SystemC-AMS的框架，它将ML模型作为一等的定时数据流（TDF）组件，通过标准化的即插即用接口进行集成。SAMpLE提供两个执行后端：用于轻量级模型在线训练的原生C++后端，以及用于执行外部开发模型的离线后端，无需在C++中重新实现或手动集成步骤。该框架采用ONNX作为标准模型交换格式，以实现将外部训练的ML模型集成到SystemC-AMS仿真中，并允许在同一测试平台、数据集和仿真工作流程中评估不同的基于ML的解决方案。模块化设计以及统一且可复现的环境将支持SAMpLE未来向新模型扩展，而无需修改SystemC-AMS结构。
英文摘要
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.
