Optimisation Techniques for Compact CNN on Embedded Systems for Gesture Recognition

Embedded applications are increasingly prevalent in various domains, from consumer electronics to industrial automation and smart cities. With the advances in integrated circuit manufacturing technologies, low-power chips can now execute complex algorithms, including machine learning models. However, the computational constraints of embedded devices require compact and efficient neural network models, as well as software frameworks and optimisation techniques tailored to their hardware resources. This study investigates the implementation of Convolutional Neural Network (CNN) models for gesture recognition on an STM32F4 microcontroller, by exploring the impact of freezing layers, fine-tuning and pruning techniques on pre-trained CNN models. The results demonstrate that fine-tuning and freezing layers improve accuracy by up to 18%. Additionally, freezing layers by 10% and 20% improved the accuracy. Finally, we demonstrate that pruning reduced the model size by 90%, enabling it to perform gesture recognition on small devices. These findings are significant for developing software and optimisation techniques for embedded systems, particularly in the context of the Internet of Things.