  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.
