Benchmark Studies of Machine Learning Inference using SOFIE

¹ European Organization for Nuclear Research (CERN), Geneva, Switzerland
² The University of Manchester, Manchester, United Kingdom
³ University of Thessaly, Thessaly, Greece
⁴ Veermata Jijabai Technological Institute, Mumbai, India
⁵ Georg August University of Göttingen, Göttingen, Germany

^* e-mail: lorenzo.moneta@cern.ch

Published online: 7 October 2025

Abstract

SOFIE is a fast Machine Learning inference engine developed at CERN, capable of translating trained deep learning models—provided in ONNX, Keras, or PyTorch formats—into C++ code for efficient inference. The generated code has minimal dependencies, making it easily integrable into the data processing and analysis workflows of high-energy physics (HEP) experiments.

This study presents a comprehensive benchmark analysis of SOFIE against leading machine learning frameworks for model evaluation, including PyTorch, TensorFlow XLA, and ONNX Runtime. We focus on evaluating their performance in HEP applications, particularly for typical models such as Graph Neural Networks for jet tagging, and Variational Autoencoders and Generative Adversarial Networks for fast simulation. Our assessment considers key factors such as computational speed, memory usage, scalability, and ease of integration with existing HEP software ecosystems. Through this comparative study, we aim to provide insights that help the HEP community select the most suitable framework for their specific needs.