  We present an interpretable implementation of the autoencoding algorithm,
used as an anomaly detector, built with a forest of deep decision trees on
FPGA, field programmable gate arrays. Scenarios at the Large Hadron Collider at
CERN are considered, for which the autoencoder is trained using known physical
processes of the Standard Model. The design is then deployed in real-time
trigger systems for anomaly detection of unknown physical processes, such as
the detection of rare exotic decays of the Higgs boson. The inference is made
with a latency value of 30 ns at percent-level resource usage using the Xilinx
Virtex UltraScale+ VU9P FPGA. Our method offers anomaly detection at low
latency values for edge AI users with resource constraints.
