NEURAL NETWORK DESIGN FOR ENERGY-CONSTRAINED MICROCONTROLLER SYSTEMS

Abstract

ABSTRACT
Machine learning (ML) and deep learning (DL) are advancing rapidly due to improvements in computational capabilities, the availability of large-scale data, and continuous refinement of learning algorithms. A major focus of current research is the creation of models that are more efficient, flexible, and easier to interpret. Techniques such as federated learning and edge computing are gaining increased attention, as they enable decentralized data processing while strengthening data privacy. Transformer-based models, originally developed for natural language processing, have been successfully extended to domains such as computer vision and time-series modeling, where they have shown superior performance.
At the same time, the convergence of quantum computing and ML presents new opportunities to address highly complex problems through enhanced computational speed. The growing importance of Explainable Artificial Intelligence (XAI) highlights the demand for transparency and accountability in deep learning systems, aiming to mitigate the limitations of black-box models. Furthermore, the integration of ML with emerging technologies including the Internet of Things (IoT), blockchain, and 5G communication networks is driving innovation in areas such as smart systems, autonomous technologies, and digital healthcare. Ongoing research into hybrid approaches that merge symbolic reasoning with neural networks is also contributing to more reliable and context-aware decision-making. Together, these developments represent a significant advancement in ML and DL architectures, with the potential to address complex real-world challenges and support the development of sustainable, intelligent, and autonomous systems.