  Deep learning, through the use of neural networks, has demonstrated
remarkable ability to automate many routine tasks when presented with
sufficient data for training. The neural network architecture (e.g. number of
layers, types of layers, connections between layers, etc.) plays a critical
role in determining what, if anything, the neural network is able to learn from
the training data. The trend for neural network architectures, especially those
trained on ImageNet, has been to grow ever deeper and more complex. The result
has been ever increasing accuracy on benchmark datasets with the cost of
increased computational demands. In this paper we demonstrate that neural
network architectures can be automatically generated, tailored for a specific
application, with dual objectives: accuracy of prediction and speed of
prediction. Using MENNDL--an HPC-enabled software stack for neural architecture
search--we generate a neural network with comparable accuracy to
state-of-the-art networks on a cancer pathology dataset that is also $16\times$
faster at inference. The speedup in inference is necessary because of the
volume and velocity of cancer pathology data; specifically, the previous
state-of-the-art networks are too slow for individual researchers without
access to HPC systems to keep pace with the rate of data generation. Our new
model enables researchers with modest computational resources to analyze newly
generated data faster than it is collected.
