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:深神经网络对许多人工智能(AI)任务的显著积极影响已导致各种高性能算法以及专门处理器和加速器的开发。在本文件中,我们通过表明在传统处理器结构中现代实现通用矩阵乘法(GEMM)的现代实现原则对于在诸如嵌入Xilinx Versal平台的AI引擎(AIE)瓷砖等异域加速器的深度学习(DL)中产生的操作类型取得高性能也是有效的。特别是,我们通过在Xilinx Versal VC190号模型上原型实施GEMM内核的实验结果,为16位整形操作提供了接近16位AIE的预期理论峰值的86.7%的性能。