  The remarkable positive impact of Deep Neural Networks on many Artificial
Intelligence (AI) tasks has led to the development of various high performance
algorithms as well as specialized processors and accelerators. In this paper we
address this scenario by demonstrating that the principles underlying the
modern realization of the general matrix multiplication (GEMM) in conventional
processor architectures, are also valid to achieve high performance for the
type of operations that arise in deep learning (DL) on an exotic accelerator
such as the AI Engine (AIE) tile embedded in Xilinx Versal platforms. In
particular, our experimental results with a prototype implementation of the
GEMM kernel, on a Xilinx Versal VCK190, delivers performance close to 86.7% of
the theoretical peak that can be expected on an AIE tile, for 16-bit integer
operands.
