Performance Analysis of GEMM Workloads on the AMD Versal Platform

AMD Versal is a new heterogeneous computing hardware architecture comprised of adaptive intelligence (AI) engines, programmable logic, and a processing system. General Matrix Multiplication (GEMM) is the fundamental building block of modern deep learning (DL) applications such as ChatGPT, and GEMM workloads can be mapped onto Versal in different ways, each with distinct trade-offs. This paper presents a thorough analysis of GEMM workloads of different shapes and sizes, showcasing performance artifacts associated with the AMD Versal architecture. Focusing on the unique aspects of the Versal architecture, multiple research questions related to performance scaling, sensitivity, and efficiency are explored. This paper aims to assist developers in the FPGA community looking to implement GEMM on AMD Versal by providing guidelines and insights for enhancing performance.