  Accurate particle identification (PID) is one of the most important aspects
of the LHCb experiment. Modern machine learning techniques such as neural
networks (NNs) are efficiently applied to this problem and are integrated into
the LHCb software. In this research, we discuss novel applications of neural
network speed-up techniques to achieve faster PID in LHC upgrade conditions. We
show that the best results are obtained using variational dropout
sparsification, which provides a prediction (feedforward pass) speed increase
of up to a factor of sixteen even when compared to a model with shallow
networks.
