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.

翻译：深度神经网络对众多人工智能（AI）任务的显著积极影响，推动了各种高性能算法以及专用处理器和加速器的发展。本文通过证明传统处理器架构中通用矩阵乘法（GEMM）的现代实现原理，同样适用于在Xilinx Versal平台内置的AI引擎（AIE）单元这类特殊加速器上实现深度学习（DL）相关运算的高性能，以此应对这一研究背景。具体而言，我们在Xilinx Versal VCK190上对GEMM核心进行原型实现的实验结果表明，针对16位整数操作数，其性能可达AIE单元理论峰值性能的86.7%。