Deep learning model optimization for faster inference using multi-task learning for embedded systems

Abstract

The research aims to develop and optimize a deep learning model for faster inference using multi-task learning for embedded systems. Experiments using face photos and sound in the form of a spectrogram were prepared to verify the model's performance in recognizing a person and their emotional state. Research has shown that in IoT devices, the inference is faster when a multi-tasking model is used compared to a system based on several models, each responsible for inferring one thing.