TinyML Model

TinyML focuses on deploying machine learning models, primarily neural networks (including CNNs and Transformers), onto resource-constrained microcontrollers. Current research emphasizes optimizing model architectures and algorithms for minimal memory footprint, low power consumption, and fast inference speeds, often employing techniques like model compression, quantization, and efficient kernel designs. This field is significant for enabling intelligent applications in resource-limited environments, impacting diverse sectors such as healthcare, environmental monitoring, and industrial automation through the development of energy-efficient, privacy-preserving edge devices.