Model extraction attacks pose a major threat to the confidentiality and integrity of deep neural networks, particularly in embedded systems where physical access enables additional attack vectors. This talk investigates how side-channel analysis can be leveraged to significantly enhance neural network extraction techniques beyond traditional software-only approaches. We introduce a novel paradigm for model extraction based on the subdivision of neural networks into smaller components at activation boundaries. By combining side-channel information with cryptanalytic techniques, this approach overcomes key limitations of prior methods, enabling the extraction of more complex architectures that were previously out of reach. We then analyze the impact of 8-bit quantization on extraction attacks, showing that while it can degrade the effectiveness of existing cryptanalytic methods, it does not provide sufficient protection in some context. These results demonstrate that neural network extraction in embedded contexts must be considered through both algorithmic and physical perspectives, and highlight the need for defenses that account for side-channel leakage.