  Bayesian Optimization Mixed-Precision Neural Architecture Search (BOMP-NAS)
is an approach to quantization-aware neural architecture search (QA-NAS) that
leverages both Bayesian optimization (BO) and mixed-precision quantization (MP)
to efficiently search for compact, high performance deep neural networks. The
results show that integrating quantization-aware fine-tuning (QAFT) into the
NAS loop is a necessary step to find networks that perform well under
low-precision quantization: integrating it allows a model size reduction of
nearly 50\% on the CIFAR-10 dataset. BOMP-NAS is able to find neural networks
that achieve state of the art performance at much lower design costs. This
study shows that BOMP-NAS can find these neural networks at a 6x shorter search
time compared to the closest related work.
