  The Belle II experiment will go into operation at the upgraded SuperKEKB
collider in 2016. SuperKEKB is designed to deliver an instantaneous luminosity
$\mathcal{L}=8\times10^{35}\,\mathrm{cm}^{-2}\,\mathrm{s}^{-1}$. The experiment
will therefore have to cope with a much larger machine background than its
predecessor Belle, in particular from events outside of the interaction region.
We present the concept of a track trigger, based on a neural network approach,
that is able to suppress a large fraction of this background by reconstructing
the $z$ (longitudinal) position of the event vertex within the latency of the
first level trigger. The trigger uses the hit information from the Central
Drift Chamber (CDC) of Belle II within narrow cones in polar and azimuthal
angle as well as in transverse momentum ("sectors"), and estimates the
$z$-vertex without explicit track reconstruction. The preprocessing for the
track trigger is based on the track information provided by the standard CDC
trigger. It takes input from the 2D track finder, adds information from the
stereo wires of the CDC, and finds the appropriate sectors in the CDC for each
track. Within the sector, the $z$-vertex is estimated by a specialized neural
network, with the drift times from the CDC as input and a continuous output
corresponding to the scaled $z$-vertex. The neural algorithm will be
implemented in programmable hardware. To this end a Virtex 7 FPGA board will be
used, which provides at present the most promising solution for a fully
parallelized implementation of neural networks or alternative multivariate
methods. A high speed interface for external memory will be integrated into the
platform, to be able to store the $\mathcal{O}(10^9)$ parameters required. The
contribution presents the results of our feasibility studies and discusses the
details of the envisaged hardware solution.
