  The efficacy of deep learning has resulted in its use in a growing number of
applications. The Volta graphics processor unit (GPU) architecture from NVIDIA
introduced a specialized functional unit, the "tensor core", that helps meet
the growing demand for higher performance for deep learning. In this paper we
study the design of the tensor cores in NVIDIA's Volta and Turing
architectures. We further propose an architectural model for the tensor cores
in Volta. When implemented a GPU simulator, GPGPU-Sim, our tensor core model
achieves 99.6\% correlation versus an NVIDIA Titan~V GPU in terms of average
instructions per cycle when running tensor core enabled GEMM workloads. We also
describe support added to enable GPGPU-Sim to run CUTLASS, an open-source CUDA
C++ template library providing customizable GEMM templates that utilize tensor
cores.
