FPGA or GPU? Analyzing comparative research for application-specific guidance

TL;DR

This paper synthesizes research comparing FPGAs and GPUs to provide application-specific guidance, focusing on performance, energy efficiency, and programmability for domain-specific workloads.

Contribution

It offers a comprehensive analysis categorizing studies and providing actionable recommendations for selecting between FPGAs and GPUs based on application needs.

Findings

FPGAs excel in energy efficiency for specific tasks

GPUs generally provide higher raw performance

Guidelines for matching hardware to application domains

Abstract

The growing complexity of computational workloads has amplified the need for efficient and specialized hardware accelerators. Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) have emerged as prominent solutions, each excelling in specific domains. Although there is substantial research comparing FPGAs and GPUs, most of the work focuses primarily on performance metrics, offering limited insight into the specific types of applications that each accelerator benefits the most. This paper aims to bridge this gap by synthesizing insights from various research articles to guide users in selecting the appropriate accelerator for domain-specific applications. By categorizing the reviewed studies and analyzing key performance metrics, this work highlights the strengths, limitations, and ideal use cases for FPGAs and GPUs. The findings offer actionable recommendations, helping researchers and practitioners navigate trade-offs in performance, energy efficiency, and programmability.