There has been a steady increase in technologies that leverage Deep Learning (DL) techniques on resource-constrained devices for real-time processing. While DL techniques are adept at recognition tasks, their performance depends on the training process. Training data is seldom fully representative of the deployment scenario, requiring retraining to preserve accuracy. Many works have proposed on-device learning techniques that enable training DL techniques like Convolutional Neural Networks (CNNs) on microcontroller units (MCUs), without requiring data to be sent to the cloud. These methods have yielded promising accuracy improvements while training with low memory footprints. However, limited research has been done on the energy implications of doing so.

This thesis presents methods for energy-aware on-device learning on MCUs. It leverages the principle of updating specific layers of a CNN, proposed in past works, to fit memory constraints. We propose an energy-accuracy trade-off objective based on computational costs (in MACs) and accuracy improvement to select which layers to train. Furthermore, we demonstrate how computationally light search algorithms can adequately maximize the newly defined objective for layer selection. Evaluations show that our approach saves up to 200mJ of energy on-device while yielding simulation accuracies similar to a recent study under the same conditions.