RTNNIgen – An Open-Source PLC Library and Python Generator Converting Keras Models to Realtime Capable Structured Text
Recent advances in AI research are boosting the relevance of realtime neural networks in automation technology. To facilitate the deployment of trained neural networks for realtime applications, where hard limits on upper cycle time are required, this paper contributed a two-fold approach: A Python library for parsing Keras sequential models to structured text using a single function-call, which can be run directly on industrial PLCs and a structured text library to encapsulate reoccurring code and to provide a readable, tested interface for the generated code. The resulting models are experimentally validated regarding their inference time and precision of the outputs.
Authors
- Armin Lechler (ORCID: https://orcid.org/0000-0002-4073-1487)
- Alexander Verl (ORCID: https://orcid.org/0000-0002-2548-6620)
- Christoph Hinze (ORCID: https://orcid.org/0000-0001-6615-5003)
- Zhong Zhou (ORCID: https://orcid.org/0000-0002-5825-7517)
- H. Xu (ORCID: https://orcid.org/0000-0002-0006-0164)
Institutions
- University of Stuttgart (DE)
Publication Details
- Published
- 2024-11-03
- DOI
- https://doi.org/10.1109/iecon55916.2024.10905775
- Primary Topic
- Semantic Web and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00