32 Bit Microcontrollers

32-bit microcontrollers are increasingly central to TinyML, enabling the deployment of machine learning models on resource-constrained devices. Current research emphasizes optimizing model architectures (like CNNs and Transformers) and algorithms for reduced memory footprint, latency, and energy consumption, often employing techniques such as quantization, pruning, and adaptive inference. This focus stems from the growing need for efficient, on-device intelligence in applications ranging from wearable health monitoring to industrial automation, driving innovation in both hardware and software solutions for embedded AI.