  Current state-of-the-art Neural Architecture Search (NAS) methods neither
efficiently scale to multiple hardware platforms, nor handle diverse
architectural search-spaces. To remedy this, we present DONNA (Distilling
Optimal Neural Network Architectures), a novel pipeline for rapid, scalable and
diverse NAS, that scales to many user scenarios. DONNA consists of three
phases. First, an accuracy predictor is built using blockwise knowledge
distillation from a reference model. This predictor enables searching across
diverse networks with varying macro-architectural parameters such as layer
types and attention mechanisms, as well as across micro-architectural
parameters such as block repeats and expansion rates. Second, a rapid
evolutionary search finds a set of pareto-optimal architectures for any
scenario using the accuracy predictor and on-device measurements. Third,
optimal models are quickly finetuned to training-from-scratch accuracy. DONNA
is up to 100x faster than MNasNet in finding state-of-the-art architectures
on-device. Classifying ImageNet, DONNA architectures are 20% faster than
EfficientNet-B0 and MobileNetV2 on a Nvidia V100 GPU and 10% faster with 0.5%
higher accuracy than MobileNetV2-1.4x on a Samsung S20 smartphone. In addition
to NAS, DONNA is used for search-space extension and exploration, as well as
hardware-aware model compression.
