Towards Ensuring Software Interoperability Between Deep Learning Frameworks

With the widespread of systems incorporating multiple deep learning models, ensuring interoperability between target models has become essential. However, due to the unreliable performance of existing model conversion solutions, it is still challenging to ensure interoperability between the models d...

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Bibliographic Details
Published in:Journal of Artificial Intelligence and Soft Computing Research 2023-10, Vol.13 (4), p.215-228
Main Authors: Lee, Youn Kyu, Park, Seong Hee, Lim, Min Young, Lee, Soo-Hyun, Jeong, Jongwook
Format: Article
Language:English
Subjects:
Conversion
Deep learning
Interoperability
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Summary:With the widespread of systems incorporating multiple deep learning models, ensuring interoperability between target models has become essential. However, due to the unreliable performance of existing model conversion solutions, it is still challenging to ensure interoperability between the models developed on different deep learning frameworks. In this paper, we propose a systematic method for verifying interoperability between pre- and post-conversion deep learning models based on the validation and verification approach. Our proposed method ensures interoperability by conducting a series of systematic verifications from multiple perspectives. The case study confirmed that our method successfully discovered the interoperability issues that have been reported in deep learning model conversions.
ISSN:2449-6499
2449-6499
DOI:10.2478/jaiscr-2023-0016