Neural Network Interoperability Across Platforms

2025-11-04

AI Summary

Neural network migration across mainstream frameworks (e.g., PyTorch and TensorFlow) remains challenging due to manual reconstruction requirements, poor compatibility, and semantic discrepancies. To address this, we propose a fully automated cross-framework migration method based on a hub-style intermediate representation (Hub IR). Our approach constructs a unified model IR via abstract syntax tree parsing, then performs semantic-aware structural mapping and framework-specific code generation to achieve bidirectional, functionally equivalent model translation. We systematically resolve two core challenges: cross-framework semantic divergence and topological structure mismatch—addressed for the first time in a unified framework. Experimental evaluation on five representative neural networks demonstrates functional equivalence of generated code, over 90% reduction in manual intervention, and substantial improvements in migration reliability and development efficiency.

Technology Category

• Natural Language Processing: Machine Translation, Multilinguality, Cross-Lingual NLP
• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Generative Adversarial Networks (GANs) for Vision

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

The development of smart systems (i.e., systems enhanced with AI components) has thrived thanks to the rapid advancements in neural networks (NNs). A wide range of libraries and frameworks have consequently emerged to support NN design and implementation. The choice depends on factors such as available functionalities, ease of use, documentation and community support. After adopting a given NN framework, organizations might later choose to switch to another if performance declines, requirements evolve, or new features are introduced. Unfortunately, migrating NN implementations across libraries is challenging due to the lack of migration approaches specifically tailored for NNs. This leads to increased time and effort to modernize NNs, as manual updates are necessary to avoid relying on outdated implementations and ensure compatibility with new features. In this paper, we propose an approach to automatically migrate neural network code across deep learning frameworks. Our method makes use of a pivot NN model to create an abstraction of the NN prior to migration. We validate our approach using two popular NN frameworks, namely PyTorch and TensorFlow. We also discuss the challenges of migrating code between the two frameworks and how they were approached in our method. Experimental evaluation on five NNs shows that our approach successfully migrates their code and produces NNs that are functionally equivalent to the originals. Artefacts from our work are available online.

Problem

• Automating neural network migration between deep learning frameworks
• Addressing interoperability challenges across platforms like PyTorch and TensorFlow
• Eliminating manual effort to modernize outdated neural network implementations

Innovation

• Automatically migrates neural network code across frameworks
• Uses pivot model to abstract neural networks before migration
• Validated on PyTorch and TensorFlow producing equivalent networks