Artificial Intelligence is increasingly being used in everyday devices. However, most AI systems are designed to run on powerful computers or cloud servers rather than on small, low-power devices such as microcontrollers. Running AI directly on these devices can reduce energy consumption and enable systems to operate without an internet connection. AIfES (Artificial Intelligence for Embedded Systems) is a machine learning framework that allows neural networks to be trained directly on microcontrollers. However, it currently lacks support for depthwise convolution, an important operation used in efficient neural network architectures such as MobileNet. As a result, many modern computer vision models cannot be trained within the framework.

This project extends AIfES with support for depthwise convolution and integrates the new operator into the existing training pipeline. The implementation was validated using a combination of manually verified test cases, comparisons with TensorFlow, and image classification experiments on embedded hardware. The results show that the new operator functions correctly during both inference and training. Models containing the implemented layer successfully learned classification tasks and achieved behavior similar to equivalent TensorFlow models. By adding support for depthwise convolution, this work expands the range of neural network architectures that can be trained directly on microcontrollers and contributes to making on-device AI more practical and flexible. ... Read more