  Recurrent neural networks have been shown to be effective architectures for
many tasks in high energy physics, and thus have been widely adopted. Their use
in low-latency environments has, however, been limited as a result of the
difficulties of implementing recurrent architectures on field-programmable gate
arrays (FPGAs). In this paper we present an implementation of two types of
recurrent neural network layers -- long short-term memory and gated recurrent
unit -- within the hls4ml framework. We demonstrate that our implementation is
capable of producing effective designs for both small and large models, and can
be customized to meet specific design requirements for inference latencies and
FPGA resources. We show the performance and synthesized designs for multiple
neural networks, many of which are trained specifically for jet identification
tasks at the CERN Large Hadron Collider.
