  We present a novel implementation of classification using the machine
learning / artificial intelligence method called boosted decision trees (BDT)
on field programmable gate arrays (FPGA). The firmware implementation of binary
classification requiring 100 training trees with a maximum depth of 4 using
four input variables gives a latency value of about 10 ns, which corresponds to
3 clock ticks at 320 MHz in our setup. The low timing values are achieved by
restructuring the BDT layout and reconfiguring its parameters. The FPGA
resource utilization is also kept low at a range from 0.01% to 0.2% in our
setup. A software package called fwXmachina achieves this implementation. Our
intended audience is a user of custom electronics-based trigger systems in high
energy physics experiments or anyone that needs decisions at the lowest latency
values for real-time event classification. Two problems from high energy
physics are considered, in the separation of electrons vs. photons and in the
selection of vector boson fusion-produced Higgs bosons vs. the rejection of the
multijet processes.
