======== Brevitas ======== .. toctree:: :maxdepth: 1 Setup Getting Started Tutorials Papers User Guides .. toctree:: :hidden: Settings FAQ API reference About Brevitas implements a set of building blocks at different levels of abstraction to model a reduced precision hardware data-path at training time. It provides a platform both for researchers interested in implementing new quantization-aware training techinques, as well as for practitioners interested in applying current techniques to their models. Brevitas supports a super-set of quantization schemes implemented across various frameworks and compilers under a single unified API. For certain combinations of layers and types of of quantization inference acceleration is supported by exporting to *FINN*, *onnxruntime* or *Pytorch*'s own quantized operators. Brevitas has been successfully adopted both in various research projects as well as in large-scale commercial deployments targeting CPUs, GPUs, and custom accelerators running on AMD FPGAs. The general quantization style implemented is affine quantization, with a focus on uniform quantization. Non-uniform quantization is currently not supported out-of-the-box.