  Deep learning (DL) research yields accuracy and product improvements from
both model architecture changes and scale: larger data sets and models, and
more computation. For hardware design, it is difficult to predict DL model
changes. However, recent prior work shows that as dataset sizes grow, DL model
accuracy and model size grow predictably. This paper leverages the prior work
to project the dataset and model size growth required to advance DL accuracy
beyond human-level, to frontier targets defined by machine learning experts.
Datasets will need to grow $33$--$971 \times$, while models will need to grow
$6.6$--$456\times$ to achieve target accuracies.
  We further characterize and project the computational requirements to train
these applications at scale. Our characterization reveals an important
segmentation of DL training challenges for recurrent neural networks (RNNs)
that contrasts with prior studies of deep convolutional networks. RNNs will
have comparatively moderate operational intensities and very large memory
footprint requirements. In contrast to emerging accelerator designs,
large-scale RNN training characteristics suggest designs with significantly
larger memory capacity and on-chip caches.
