  The extended physics program of the SuperCDMS SNOLAB dark matter search
experiment aims to maximize the sensitivity to low-mass dark matter. To realize
this, an upgrade of the existing level-1 trigger of the data acquisition system
is proposed by making use of a recurrent neural network to be implemented on
the trigger FPGA. This provides an improved amplitude estimator and
signal-noise discriminator based on the combined information of filtered traces
from individual detector channels. The architecture and configuration of this
neural trigger are discussed in this article, and the improvements in key
performance indicators such as the efficiency, resolution, and noise rate are
quantified based on signal simulations and noise data. Based on the findings in
this proof of concept, the trigger threshold is expected to be lowered by ~22%.
