  This paper presents the custom implementation, optimization, and performance
evaluation of convolutional neural networks on field programmable gate arrays,
for the purposes of accelerating deep neural network inference on large,
two-dimensional image inputs. The targeted application is that of data
selection for high-resolution particle imaging detectors, and in particular
liquid argon time projection chamber detectors, such as that employed by the
future Deep Underground Neutrino Experiment. We motivate this particular
application based on the excellent performance of deep neural networks on
classifying simulated raw data from the DUNE LArTPC, combined with the need for
power-efficient data processing in the case of remote, long-term, and
limited-access operating detector conditions.
