Look-Up mAI GeMM: Increasing AI GeMMs Performance by Nearly 2.5x via
  msGeMM
Saeed Maleki
AI models are increasing in size and recent advancement in the community has
shown that unlike HPC applications where double precision datatype are
required, lower-precision datatypes such as fp8 or int4 are sufficient to bring
the same model quality both for training and inference. Following these trends,
GPU vendors such as NVIDIA and AMD have added hardware support for fp16, fp8
and int8 GeMM operations with an exceptional performance via Tensor Cores.
However, this paper proposes a new algorithm called msGeMM which shows that AI
models with low-precision datatypes can run with ~2.5x fewer multiplication and
add instructions. Efficient implementation of this algorithm requires special
CUDA cores with the ability to add elements from a small look-up table at the
rate of Tensor Cores.