Recent research has demonstrated that reduced precision data formats (e.g., formats using 8 bits or less per number) have the potential to greatly improve the performance and energy efficiency of AI training and inference with negligible impact on accuracy. Harnessing the full potential of these reduced precision formats, however, requires sophisticated software to quantize higher precision numbers to reduced precision and emulate the use of reduced precision formats prior for research and advanced development. In this talk, we describe Brevitas, which is a PyTorch library for neural network quantization and emulation with support for both post-training quantization (PTQ) and quantization-aware training (QAT). We give an overview of Brevitas supports for advanced data formats and present experimental results from using these formats.

Speaker: Michael Schulte, Senior Fellow, AMD Research and Advanced Development