Towards Ensuring Software Interoperability Between Deep Learning Frameworksopen access
- Authors
- Lee, Youn Kyu; Park, Seong Hee; Lim, Min Young; Lee, Soo-Hyun; Jeong, Jongwook
- Issue Date
- 1-Oct-2023
- Publisher
- SCIENDO
- Keywords
- deep learning; interoperability; validation&verification; deep learning frameworks; model conversion
- Citation
- JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH, v.13, no.4, pp 215 - 228
- Pages
- 14
- Journal Title
- JOURNAL OF ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING RESEARCH
- Volume
- 13
- Number
- 4
- Start Page
- 215
- End Page
- 228
- URI
- https://scholarworks.bwise.kr/hongik/handle/2020.sw.hongik/32116
- DOI
- 10.2478/jaiscr-2023-0016
- ISSN
- 2083-2567
2449-6499
- Abstract
- 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.
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Collections - College of Engineering > Computer Engineering > Journal Articles
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