Implementation of a Neural Network Model on the Aurora Operating System Using ARM Architecture

Deploying and training deep neural networks on mobile devices is a challenging task as machine learning applications increasingly require real-time data processing and on-device training capabilities. The ARM microprocessor architecture for mobile devices and embedded systems offers attractive platforms for edge machine learning. There are various machine learning libraries optimized for resource-constrained environments. This paper discusses the TensorFlow Lite library, the process of running and training machine learning models directly on ARM devices using TensorFlow Lite. It discusses the technical implementation, challenges, and optimizations required to achieve efficient training and running of neural network models using mobile devices with the Aurora operating system as an example. Finally, an example of implementing and training a neural network model on the Aurora OS in the C++ programming language without using additional libraries is given as an alternative to the TensorFlow Lite library.