Recent Developments in Transformer Inference Deployment on FPGA Platforms: A Survey

2026-09-01

AI Summary

本文探讨了基于FPGA平台的Transformer模型推理部署问题，通过系统文献综述分析了最新的进展、趋势及优化方法，旨在提高性能和能效。

Abstract

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.

Problem

• Transformer
• inference
• FPGA
• deployment
• performance

Innovation

• Transformer
• FPGA
• inference deployment
• energy efficiency
• low latency