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.

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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
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article

RTNNIgen – An Open-Source PLC Library and Python Generator Converting Keras Models to Realtime Capable Structured Text

Armin Lechler, Alexander Verl, Christoph Hinze, Zhong Zhou et al.
1 links
Semantic Web and Ontologies
article

RTNNIgen – An Open-Source PLC Library and Python Generator Converting Keras Models to Realtime Capable Structured Text

Armin Lechler, Alexander Verl, Christoph Hinze, Zhong Zhou, H. Xu
article en

Abstract

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.

University of Stuttgart (DE)
Openalex Percentile: Top 74%
Semantic Web and Ontologies
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