🤖 AI Summary
Existing FPGA–GPU comparative studies predominantly focus on raw performance metrics and lack domain-specific guidance for accelerator selection. Method: This paper proposes an application-oriented, fine-grained comparative framework that systematically synthesizes over 100 studies, conducting cross-domain (e.g., AI, HPC, network processing, scientific computing) classification and cross-evaluation along three dimensions: performance, energy efficiency, and programmability. Contribution/Results: The study innovatively establishes the first empirically grounded applicability boundaries for FPGAs and GPUs: FPGAs excel in low-latency, high-throughput customized pipelines and energy-constrained scenarios; GPUs are superior for massively parallel, computation-intensive workloads with stable algorithms. The resulting actionable decision-making guide enables researchers and engineers to select hardware accelerators based on domain-specific requirements, thereby bridging the gap between architectural characteristics and real-world application needs.
📝 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.