Model Converter

Model converters facilitate the transfer of models between different software frameworks or hardware platforms, aiming to improve interoperability and optimize performance. Current research focuses on automating fault detection and repair during conversion, particularly for deep learning models, employing techniques like reinforcement learning and analysis of operator sequences to improve accuracy and efficiency. This work is crucial for addressing challenges in deploying models across diverse environments and enabling broader access to advanced modeling techniques in fields ranging from power electronics to natural language processing.