  In the transition to Run 3 in 2021, LHCb will undergo a major luminosity
upgrade, going from 1.1 to 5.6 expected visible Primary Vertices (PVs) per
event, and will adopt a purely software trigger. This has fueled increased
interest in alternative highly-parallel and GPU friendly algorithms for
tracking and reconstruction. We will present a novel prototype algorithm for
vertexing in the LHCb upgrade conditions. We use a custom kernel to transform
the sparse 3D space of hits and tracks into a dense 1D dataset, and then apply
Deep Learning techniques to find PV locations. By training networks on our
kernels using several Convolutional Neural Network layers, we have achieved
better than 90% efficiency with no more than 0.2 False Positives (FPs) per
event. Beyond its physics performance, this algorithm also provides a rich
collection of possibilities for visualization and study of 1D convolutional
networks. We will discuss the design, performance, and future potential areas
of improvement and study, such as possible ways to recover the full 3D vertex
information.
