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Talk13 min

C++ Code Generation for Fast Inference of Deep Learning Models in ROOT/TMVA

Sitong An · CERN

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About

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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The talk

  1. C++ Code Generation for Fast Inference of Deep Learning Models in ROOT/TMVA 2021-05-18 · 13 min · CERN's player

Credits and licence

Institution
CERN
Department
Conferences
Speaker
Sitong An
Year
2021
Language
English
Subjects
Physics, Computer science, Earth and environment
Source
CERN
Licence
CERN, as CERN states it. CERN's terms
Attribution
© CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
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