Experimental Analysis of Matrix Multiplication Functional Units
Brian J. Hickmann, Dennis Bradford · 2019
The rapid growth of AI has led to the introduction of several new hardware designs to accelerate the matrix multiplication operation at the heart of AI applications. Examples include NVIDIA's Tensor Core*, Google's TPU*, and Intel's Neural Compute Stick*. However, the IEEE 754 standard gives significant implementation-specific flexibility in the definition of the matrix multiplication operation and the precision and compatibility of these new accelerators is not well documented. This paper describes a method exploiting the rounding modes and other features of the IEEE 754 standard in order to gain deeper insight into the design and functionality of matrix multiplication units. We apply this method to the NVIDIA V100 GPU Tensor Core* units and report our findings on the design properties and micro-architecture.