  Neural Architecture Search (NAS) has become the de-facto approach for
designing accurate and efficient networks for edge devices. Since models are
typically quantized for edge deployment, recent work has investigated
quantization-aware NAS (QA-NAS) to search for highly accurate and efficient
quantized models. However, existing QA-NAS approaches, particularly few-bit
mixed-precision (FB-MP) methods, do not scale to larger tasks. Consequently,
QA-NAS has mostly been limited to low-scale tasks and tiny networks. In this
work, we present an approach to enable QA-NAS (INT8 and FB-MP) on large-scale
tasks by leveraging the block-wise formulation introduced by block-wise NAS. We
demonstrate strong results for the semantic segmentation task on the Cityscapes
dataset, finding FB-MP models 33% smaller and INT8 models 17.6% faster than
DeepLabV3 (INT8) without compromising task performance.
