Hardware-Aware Machine Learning Methods for Medical Edge Devices

Abstract

Real-time embedded edge devices are of great importance in many applications, such as autonomous driving, robots or smartwatches. Additionally, various medical procedures involve embedded, small in-body sensor edge devices. Equipping such devices with artificial intelligence can improve the procedures by incorporating new functionalities. However, this is often accompanied by a high demand for energy and computational resources if the models are not optimized accordingly. For some applications involving in-body edge devices equipped with machine learning models, the neural network parameters must be stored directly on-device and the model executed locally. Notably, resource-constrained devices impose stringent requirements on machine learning models in respect to on-chip area and electrical energy consumption. These restrictions need to be considered in the final model design. Deep learning methods involving neural networks with millions or even billions of parameters or operations that cannot simply be transferred to hardware, are not a viable solution. Furthermore, there is a necessity of using lightweight, quantized models in fixed-point representation to realize efficient inference on hardware. Storing model parameters in lower precision potentially impairs the overall performance of the classifier, which needs to be addressed by dedicated techniques, such as hardware-aware training. Additionally, potential challenges involve general data sparsity and class imbalances, which often occur in medical datasets since pathologies are naturally underrepresented compared to healthy samples. Importantly, if well-designed, machine-learning-based decision models provide new energy-saving functionalities that can lower the energy demand of the whole system. This thesis specifically addresses these problems by examining two important medical applications: the Video Capsule Endoscopy, a methodology to investigate the otherwise inaccessible small intestine using a small, pill-sized capsule and seizure detection using neuroimplants intended for drug-resistant epilepsy patients. The main objective of this thesis is to overcome the described challenges and design artificial intelligence-based classification models suitable for tiny edge devices as present in both introduced medical applications. It is further expected that other medical applications can benefit from the presented methods as well. Overall, this work is dedicated to the development of hardware-aware, specialized machine learning techniques for the Video Capsule Endoscopy and preictal seizure detection. The approaches are tailored for an on-device application, providing the groundwork for future innovations and enhancements, such as an actively controlled capsule. For both applications, hybrid models are proposed, combining machine learning classifiers based on deep neural networks with time-series techniques, such as Hidden Markov Models, to solve these challenges. The resulting methods are accurate, highly efficient and are verified on FPGA-based hardware demonstrators to measure their power consumption. This enhances both medical procedures involving low-power edge devices without increasing the energy demand of the whole system.