TinyML is bringing deep learning models to microcontrollers


This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Deep learning models owe their initial success to large servers with large amounts of memory and clusters of GPUs. The promises of deep learning gave rise to an entire industry of cloud computing services for deep neural networks. Consequently, very large neural networks running on virtually unlimited cloud resources became very popular, especially among wealthy tech companies that can foot the bill. But at the same time, recent years have also seen a reverse trend, a concerted effort to create machine learning models for edge devices.