Tiny Machine Learning: Progress and Futures

arXiv:2403.19076v1 Announce Type: new
Abstract: Tiny Machine Learning (TinyML) is a new frontier of machine learning. By squeezing deep learning models into billions of IoT devices and microcontrollers (MCUs), we expand the scope of AI applications and enable ubiquitous intelligence. However, TinyML is challenging due to hardware constraints: the tiny memory resource makes it difficult to hold deep learning models designed for cloud and mobile platforms. There is also limited compiler and inference engine support for bare-metal devices. Therefore, we …

abstract ai applications applications arxiv constraints cs.ai cs.cv cs.lg deep learning devices expand futures hardware however intelligence iot machine machine learning mcus memory microcontrollers progress tinyml type