  The Global Event Processor (GEP) FPGA is an area-constrained,
performance-critical element of the Large Hadron Collider's (LHC) ATLAS
experiment. It needs to very quickly determine which small fraction of detected
events should be retained for further processing, and which other events will
be discarded. This system involves a large number of individual processing
tasks, brought together within the overall Algorithm Processing Platform (APP),
to make filtering decisions at an overall latency of no more than 8ms.
Currently, such filtering tasks are hand-coded implementations of standard
deterministic signal processing tasks.
  In this paper we present methods to automatically create machine learning
based algorithms for use within the APP framework, and demonstrate several
successful such deployments. We leverage existing machine learning to FPGA
flows such as hls4ml and fwX to significantly reduce the complexity of
algorithm design. These have resulted in implementations of various machine
learning algorithms with latencies of 1.2us and less than 5% resource
utilization on an Xilinx XCVU9P FPGA. Finally, we implement these algorithms
into the GEP system and present their actual performance.
  Our work shows the potential of using machine learning in the GEP for
high-energy physics applications. This can significantly improve the
performance of the trigger system and enable the ATLAS experiment to collect
more data and make more discoveries. The architecture and approach presented in
this paper can also be applied to other applications that require real-time
processing of large volumes of data.
