  Three machine learning models are used to perform jet origin classification.
These models are optimized for deployment on a field-programmable gate array
device. In this context, we demonstrate how latency and resource consumption
scale with the input size and choice of algorithm. Moreover, the models
proposed here are designed to work on the type of data and under the foreseen
conditions at the CERN LHC during its high-luminosity phase. Through
quantization-aware training and efficient synthetization for a specific field
programmable gate array, we show that $O(100)$ ns inference of complex
architectures such as Deep Sets and Interaction Networks is feasible at a
relatively low computational resource cost.
