Some microcontrollers are powerful enough to usefully run Machine Learning (ML) models on audio, motion or even image data. Even though the practical applications of this are not yet clear, there is considerable interest in Edge ML.
TensorFlow is a popular framework for training and running ML models, and there are good resources for training and miniaturising TensorFlow models. This talk is an introduction to using these models in an Embedded Rust project, and briefly walks through the process of instantiating a model, setting input tensors, running inference and reading outputs. We will use a Rust crate that wraps the C API, and this talk will outline how this crate works and how it catches many common errors at compile time.