  Neural architecture search can discover neural networks with good
performance, and One-Shot approaches are prevalent. One-Shot approaches
typically require a supernet with weight sharing and predictors that predict
the performance of architecture. However, the previous methods take much time
to generate performance predictors thus are inefficient. To this end, we
propose FOX-NAS that consists of fast and explainable predictors based on
simulated annealing and multivariate regression. Our method is
quantization-friendly and can be efficiently deployed to the edge. The
experiments on different hardware show that FOX-NAS models outperform some
other popular neural network architectures. For example, FOX-NAS matches
MobileNetV2 and EfficientNet-Lite0 accuracy with 240% and 40% less latency on
the edge CPU. FOX-NAS is the 3rd place winner of the 2020 Low-Power Computer
Vision Challenge (LPCVC), DSP classification track. See all evaluation results
at https://lpcv.ai/competitions/2020. Search code and pre-trained models are
released at https://github.com/great8nctu/FOX-NAS.
