A Methodology to Generate Efficient Neural Networks for Classification of Scientific Datasets

Neural networks (NNs) are increasingly utilized in high-throughput scientific workflows. In this context, NN efficiency is essential for successful workflow management.  We use a multi-objective Neural Architecture Search (NAS), NSGA-Net, to search for highly accurate NNs while optimizing for efficient use of computational resources by minimizing FLoating-point Operations Per Second (FLOPS). We define a domain-agnostic methodology to generate NNs with the support of NSGA-Net, select promising NNs that balance accuracy and FLOPS usage, and refine a subset of NNs in order to curate networks suitable for efficient data analysis. We apply this methodology to a protein diffraction use case. Preliminary results show NNs that efficiently classify the conformation of proteins with a final accuracy of 97.7% or higher and using only 187 FLOPS.