Sets are all you need: Ultrafast jet classification on FPGAs for HL-LHC
Patrick Odagiu Zhiqiang Que Javier Duarte Johannes Haller Gregor Kasieczka Artur Lobanov Vladimir Loncar Wayne Luk Jennifer Ngadiuba Maurizio Pierini Philipp Rincke Arpita Seksaria Sioni Summers Andre Sznajder Alexander Tapper Thea K. Aarrestad
We study various machine learning based algorithms for performing accurate
jet flavor classification on field-programmable gate arrays and demonstrate how
latency and resource consumption scale with the input size and choice of
algorithm. These architectures provide an initial design for models that could
be used for tagging at the CERN LHC during its high-luminosity phase. The
high-luminosity upgrade will lead to a five-fold increase in its instantaneous
luminosity for proton-proton collisions and, in turn, higher data volume and
complexity, such as the availability of jet constituents. Through
quantization-aware training and efficient hardware implementations, we show
that O(100) ns inference of complex architectures such as deep sets and
interaction networks is feasible at a low computational resource cost.