  This paper introduces neural architecture search (NAS) for the automatic
discovery of small models for keyword spotting (KWS) in limited resource
environments. We employ a differentiable NAS approach to optimize the structure
of convolutional neural networks (CNNs) to maximize the classification accuracy
while minimizing the number of operations per inference. Using NAS only, we
were able to obtain a highly efficient model with 95.4% accuracy on the Google
speech commands dataset with 494.8 kB of memory usage and 19.6 million
operations. Additionally, weight quantization is used to reduce the memory
consumption even further. We show that weight quantization to low bit-widths
(e.g. 1 bit) can be used without substantial loss in accuracy. By increasing
the number of input features from 10 MFCC to 20 MFCC we were able to increase
the accuracy to 96.3% at 340.1 kB of memory usage and 27.1 million operations.
