  Pushing forward the compute efficacy frontier in deep learning is critical
for tasks that require frequent model re-training or workloads that entail
training a large number of models. We introduce SliceOut -- a dropout-inspired
scheme designed to take advantage of GPU memory layout to train deep learning
models faster without impacting final test accuracy. By dropping contiguous
sets of units at random, our method realises training speedups through (1) fast
memory access and matrix multiplication of smaller tensors, and (2) memory
savings by avoiding allocating memory to zero units in weight gradients and
activations. At test time, turning off SliceOut performs an implicit ensembling
across a linear number of architectures that preserves test accuracy. We
demonstrate 10-40% speedups and memory reduction with Wide ResNets,
EfficientNets, and Transformer models, with minimal to no loss in accuracy.
This leads to faster processing of large computational workloads overall, and
significantly reduce the resulting energy consumption and CO2emissions.
