发表机构
University of Twente; Faculty of Engineering Technology, University of Twente（特温特大学; 特温特大学工程技术学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该综述针对Transformer推理部署需求，分析FPGA平台的最新进展、设计选择与优化技术，为学术界和工业界研究人员提供指南。
AI 中文摘要
随着基于Transformer架构的机器学习模型应用不断快速增长，对其部署能力的需求日益迫切。此处的部署能力指运算性能方面，如吞吐量和延迟，以及能效方面，如能耗。对于此类模型的推理任务，专用硬件加速器是中央处理器（CPU）和图形处理器（GPU）等常见部署选择的极具吸引力的替代方案。现场可编程门阵列（FPGA）平台就是这类替代加速器的一个例子，它具备实现灵活性、能效、更低延迟以及适合现场部署的特点。本文研究了FPGA平台上Transformer推理的最新进展、趋势和设计选择，通过系统的文献综述，提取并深入分析了优选的实现与优化技术。本研究及提供的主题分类可作为学术界和工业界研究人员的指南。
英文摘要
With the rapid and continuous growth in the incorporation of machine learning models based on the Transformer architecture, capable deployment is in high demand. In this context, capable deployment refers to operational performance aspects, e.g., throughput and latency, as well as efficiency aspects, e.g., energy consumption. When it comes to the task of inference using such models, purpose-built hardware accelerators provide a lucrative alternative to common deployment choices, such as Central Processing Units (CPUs) and Graphics Processing Units (GPUs). The Field Programmable Gate Array (FPGA) platforms category is an example of such alternative accelerators, promising implementation flexibility, energy efficiency, improved latency and suitability for on-site deployment. We investigate the most recent advances, trends, and design choices for Transformer inference on FPGA platforms. We perform a systematic literature review, extracting and delving into preferred techniques for implementation and optimisation. This study and the provided taxonomy of topics could act as a guide for researchers from the academia and industry alike.
Journal refJournal of Systems Architecture, Volume 177 (2026)
DOI：10.1016/j.sysarc.2026.103841
