Continuous and non invasive blood pressure monitoring is crucial for hypertension diagnosis and cardiovascular diseases prevention. Photoplethysmography (PPG) sensors offer a promising solution to solve this challenge, but current estimation methods lack the precision needed to meet medical standards and do diagnosis. The most promising approach is based on deep learning models that have achieved remarkable accuracy in controlled environments; on the other hand, their deployment on wearable devices faces fundamental constraints. The massive computational requirements and memory footprint of these neural networks make them unfit for edge devices, which must operate within strict power and resource limitations. Hence, the current challenge resides in maintaining high estimation performance while using more lightweight Deep Neural Network (DNN) models that can fit the constraints of ultra low power edge devices, such as smartwatches.