tinyml-papers-and-projects

Why we included this project

TinyML is still young enough that the key ideas are scattered across papers, talks, and blog posts, and this list gathers them in one place. Papers are grouped by year, so you can watch the field move from early compression tricks like pruning and quantization to the small, efficient architectures that fit on microcontrollers today. If you already build embedded inference, it is also useful for hunting down the original paper behind a familiar name such as MCUNet or TensorFlow Lite Micro. The project, benchmarking, book, course, and talk sections give you plenty of places to go deeper, which is about all you can ask from a curated reading list.