  Deep learning frameworks leverage GPUs to perform massively-parallel
computations over batches of many training examples efficiently. However, for
certain tasks, one may be interested in performing per-example computations,
for instance using per-example gradients to evaluate a quantity of interest
unique to each example. One notable application comes from the field of
differential privacy, where per-example gradients must be norm-bounded in order
to limit the impact of each example on the aggregated batch gradient. In this
work, we discuss how per-example gradients can be efficiently computed in
convolutional neural networks (CNNs). We compare existing strategies by
performing a few steps of differentially-private training on CNNs of varying
sizes. We also introduce a new strategy for per-example gradient calculation,
which is shown to be advantageous depending on the model architecture and how
the model is trained. This is a first step in making differentially-private
training of CNNs practical.
