FPGAs are rarely mentioned when discussing the implementation of large
machine learning applications, such as Large Language Models (LLMs), in the
data center. There has been much evidence showing that single FPGAs can be
competitive with GPUs in performance for some computations, especially for low
latency, and often much more efficient when power is considered. This suggests
that there is merit to exploring the use of multiple FPGAs for large machine
learning applications. The challenge with using multiple FPGAs is that there is
no commonly-accepted flow for developing and deploying multi-FPGA applications,
i.e., there are no tools to describe a large application, map it to multiple
FPGAs and then deploy the application on a multi-FPGA platform. In this paper,
we explore the feasibility of implementing large transformers using multiple
FPGAs by developing a scalable multi-FPGA platform and some tools to map large
applications to the platform. We validate our approach by designing an
efficient multi-FPGA version of the I-BERT transformer and implement one
encoder using six FPGAs as a working proof-of-concept to show that our platform
and tools work. Based on our proof-of-concept prototype and the estimations of
performance using the latest FPGAs compared to GPUs, we conclude that there can
be a place for FPGAs in the world of large machine learning applications. We
demonstrate a promising first step that shows that with the right
infrastructure and tools it is reasonable to continue to explore the possible
benefits of using FPGAs for applications such as LLMs.

我们通过开发一个可扩展的多 FPGA 平台和一些将大型应用映射到该平台的工具，探讨了使用多个 FPGA 实现大型 transformers 的可行性，并通过六个 FPGAs 的工作样例证明了我们的平台和工具的有效性，从而验证了在大型机器学习应用中使用 FPGAs 的可行性。