  Recent results at the Large Hadron Collider (LHC) have pointed to enhanced
physics capabilities through the improvement of the real-time event processing
techniques. Machine learning methods are ubiquitous and have proven to be very
powerful in LHC physics, and particle physics as a whole. However, exploration
of the use of such techniques in low-latency, low-power FPGA hardware has only
just begun. FPGA-based trigger and data acquisition (DAQ) systems have
extremely low, sub-microsecond latency requirements that are unique to particle
physics. We present a case study for neural network inference in FPGAs focusing
on a classifier for jet substructure which would enable, among many other
physics scenarios, searches for new dark sector particles and novel
measurements of the Higgs boson. While we focus on a specific example, the
lessons are far-reaching. We develop a package based on High-Level Synthesis
(HLS) called hls4ml to build machine learning models in FPGAs. The use of HLS
increases accessibility across a broad user community and allows for a drastic
decrease in firmware development time. We map out FPGA resource usage and
latency versus neural network hyperparameters to identify the problems in
particle physics that would benefit from performing neural network inference
with FPGAs. For our example jet substructure model, we fit well within the
available resources of modern FPGAs with a latency on the scale of 100 ns.
