对比FPGA与GPU在不同应用中的优劣，给出选型指导。
FPGA or GPU? Analyzing comparative research for application-specific guidance
- 通过分析多篇文献，按应用类型分类比较加速器性能。
- 发现FPGA在能效和延迟上优势明显，GPU在并行计算中更胜一筹。
- 适合需要低功耗或高定制性的系统设计者参考。
计算工作负载的复杂性日益增加，对高效专用硬件加速器的需求也随之上升。现场可编程门阵列（FPGAs）和图形处理单元（GPUs）已成为突出的解决方案，各自在特定领域表现出色。尽管已有大量研究比较FPGAs与GPUs，但多数仅关注性能指标，难以提供针对具体应用场景的择优建议。本文通过整合多篇研究的见解，旨在填补这一空白，为特定领域应用的加速器选择提供指导。通过对已发表研究进行分类并分析关键性能指标，本文揭示了FPGA与GPU的优势、局限及理想使用场景。研究结果提供了可操作的建议，帮助研究人员和实践者权衡性能、能效与可编程性之间的取舍。
原文摘要 · Abstract (English)
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.
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