FPGA or GPU? Analyzing Comparative Research for Application-Specific Guidance

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 substan...

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Bibliographic Details
Published inProceedings of IEEE Southeastcon pp. 1258 - 1263
Main Authors Purkayastha, Arnab A, Tharwani, Jay, Aggarwal, Shobhit
Format Conference Proceeding
LanguageEnglish
Published IEEE 22.03.2025
Subjects
Deep Learning
Energy efficiency
Field programmable gate arrays
FPGA
GPU
Graphics processing units
Hardware acceleration
Hardware Accelerators
High performance computing
Logic gates
Low latency communication
Memory management
Navigation
Pipelines
Programmability
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Summary: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.
ISSN:1558-058X
DOI:10.1109/SoutheastCon56624.2025.10971274