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National Repository of Grey Literature 1 records found  Search took 0.01 seconds. 
Conversion of Models between Machine Learning Frameworks for Mobile Platforms
 Pavella, Martin ; Zbořil, František (referee) ; Kočí, Radek (advisor)
Machine learning frameworks use various formats to represent and store models of deep neural networks (DNN). One of the most commonly used ones is Open Neural Network Exchange (ONNX). Developing drivers for hardware accelerators on embedded systems is expensive, and ONNX is rarely supported. The necessary software support is typically only implemented for the TensorFlow Lite (TFLite) DNN model format. Currently, the options for conversion of pre-trained ONNX models to TFLite are inadequate and produce suboptimal models. This work focuses on designing and developing a direct converter of ONNX models to TFLite, which produces as optimal models as possible. The resulting program was verified on real models in collaboration with the NXP company. The models produce identical outputs after conversion and their inference speed on target platforms is significantly higher.
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