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C++ Code Generation for Fast Inference of Deep Learning Models in ROOT/TMVA
<!--HTML-->We report the latest development in ROOT/TMVA, a new system that takes trained ONNX deep learning models and emits C++ code that can be easily included and invoked for fast inference of the model, with minimal dependency. We present an overview of the current solutions for conductin...
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Lenguaje: | eng |
Publicado: |
2021
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Acceso en línea: | http://cds.cern.ch/record/2766903 |
Sumario: | <!--HTML-->We report the latest development in ROOT/TMVA, a new system that takes trained ONNX deep learning models and emits C++ code that can be easily included and invoked for fast inference of the model, with minimal dependency. We present an overview of the current solutions for conducting inference in C++ production environment, discuss the technical details and examples of the generated code, and demonstrates its development status with a preliminary benchmark against popular tools. |
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