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

arXiv: https://arxiv.org/abs/2608.25910 · HTML: https://mlsanity.com/paper/2608.25910/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 43 references that are in the map.
- First posted on arXiv 26 Aug 2026.

## Builds on (most cited first, 7 of 7)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Data-driven MPC with stability guarantees using extended dynamic mode decomposition](https://mlsanity.com/paper/2308.00296/) (2023)
- [ESP4ML: Platform-Based Design of Systems-on-Chip for Embedded Machine Learning](https://mlsanity.com/paper/2004.03640/) (2020)
- [In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD](https://mlsanity.com/paper/2306.12900/) (2023)
- [A New Recursive Least-Squares Method with Multiple Forgetting Schemes](https://mlsanity.com/paper/1503.07338/) (2015)
- [MultiCoSim: A Python-based Multi-Fidelity Co-Simulation Framework](https://mlsanity.com/paper/2506.10869/) (2025)
- [Integrating SystemC TLM into FMI 3.0 Co-Simulations with an Open-Source Approach](https://mlsanity.com/paper/2508.20223/) (2025)

## Abstract (arXiv)

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 textbftextitSAMpLE, 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.