FPGAs-as-a-Service Toolkit (FaaST)

TL;DR

This paper introduces the first open-source FPGAs-as-a-service toolkit, demonstrating significant performance improvements for certain workloads in high energy physics compared to GPUs.

Contribution

It develops workflows to evaluate FPGA performance as a service, highlighting their potential advantages over GPUs for specific applications.

Findings

Small dense networks see an order of magnitude throughput increase with FPGAs.

Large convolutional networks have throughput comparable to GPUs.

First open-source FPGA-as-a-service toolkit introduced.

Abstract

Computing needs for high energy physics are already intensive and are expected to increase drastically in the coming years. In this context, heterogeneous computing, specifically as-a-service computing, has the potential for significant gains over traditional computing models. Although previous studies and packages in the field of heterogeneous computing have focused on GPUs as accelerators, FPGAs are an extremely promising option as well. A series of workflows are developed to establish the performance capabilities of FPGAs as a service. Multiple different devices and a range of algorithms for use in high energy physics are studied. For a small, dense network, the throughput can be improved by an order of magnitude with respect to GPUs as a service. For large convolutional networks, the throughput is found to be comparable to GPUs as a service. This work represents the first open-source FPGAs-as-a-service toolkit.