让机器学习模型无缝接入嵌入式系统仿真，提升可复用性与可比性。
SAMpLE: A SystemC-AMS Machine LEarning-based Framework for Virtual Prototyping
- 将ML模型作为标准组件集成到SystemC-AMS中，支持即插即用
- 支持在线训练轻量模型和离线运行外部训练好的模型
- 基于ONNX格式实现跨平台模型复用，适合硬件/软件协同设计者
机器学习（ML）在嵌入式系统虚拟原型中日益用于建模难以解析描述的行为。然而，将ML模型集成到虚拟平台仿真中仍依赖临时解决方案，限制了复用性、可比性和可重现性。本文提出开源的SystemC-AMS框架SAMpLE，通过标准化即插即用接口，将ML模型作为一等时序数据流（TDF）组件集成。SAMpLE提供两种执行后端：原生C++后端支持轻量模型的在线训练；离线后端可直接运行外部训练的模型，无需重写为C++或手动集成。框架采用ONNX作为标准模型交换格式，实现外部训练模型的无缝集成，并可在同一测试环境、数据集和仿真流程中评估不同ML方案。模块化设计与统一可重现环境使未来扩展新模型无需修改SystemC-AMS结构。
原文摘要 · Abstract (English)
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.
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