  Neural network (NN) models are increasingly used in scientific simulations,
AI, and other high performance computing (HPC) fields to extract knowledge from
datasets. Each dataset requires tailored NN model architecture, but designing
structures by hand is a time-consuming and error-prone process. Neural
architecture search (NAS) automates the design of NN architectures. NAS
attempts to find well-performing NN models for specialized datsets, where
performance is measured by key metrics that capture the NN capabilities (e.g.,
accuracy of classification of samples in a dataset). Existing NAS methods are
resource intensive, especially when searching for highly accurate models for
larger and larger datasets.
  To address this problem, we propose a performance estimation strategy that
reduces the resources for training NNs and increases NAS throughput without
jeopardizing accuracy. We implement our strategy via an engine called PEng4NN
that plugs into existing NAS methods; in doing so, PEng4NN predicts the final
accuracy of NNs early in the training process, informs the NAS of NN
performance, and thus enables the NAS to terminate training NNs early. We
assess our engine on three diverse datasets (i.e., CIFAR-100, Fashion MNIST,
and SVHN). By reducing the training epochs needed, our engine achieves
substantial throughput gain; on average, our engine saves $61\%$ to $82\%$ of
training epochs, increasing throughput by a factor of 2.5 to 5 compared to a
state-of-the-art NAS method. We achieve this gain without compromising
accuracy, as we demonstrate with two key outcomes. First, across all our tests,
between $74\%$ and $97\%$ of the ground truth best models lie in our set of
predicted best models. Second, the accuracy distributions of the ground truth
best models and our predicted best models are comparable, with the mean
accuracy values differing by at most .7 percentage points across all tests.
