FBGEMM
FBGEMM is a high-performance library for server-side inference, specializing in low-precision matrix multiplications and convolutions. It supports small batch sizes and uses techniques like row-wise quantization to reduce accuracy loss. The library also addresses bandwidth constraints through fusion opportunities. As a backend for PyTorch quantized operators on x86 hardware, it enhances deep learning inference. Comprehensive documentation is available for building, installation, and development.
