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# Brevitas

- Setup
- Getting Started
- Tutorials
- Settings
- API reference
- Architecture
- FAQ
- 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 Xilinx 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.