  We present FMAS, a fast multi-objective neural architecture search framework
for semantic segmentation. FMAS subsamples the structure and pre-trained
parameters of DeepLabV3+, without fine-tuning, dramatically reducing training
time during search. To further reduce candidate evaluation time, we use a
subset of the validation dataset during the search. Only the final, Pareto
non-dominated, candidates are ultimately fine-tuned using the complete training
set. We evaluate FMAS by searching for models that effectively trade accuracy
and computational cost on the PASCAL VOC 2012 dataset. FMAS finds competitive
designs quickly, e.g., taking just 0.5 GPU days to discover a DeepLabV3+
variant that reduces FLOPs and parameters by 10$\%$ and 20$\%$ respectively,
for less than 3$\%$ increased error. We also search on an edge device called
GAP8 and use its latency as the metric. FMAS is capable of finding 2.2$\times$
faster network with 7.61$\%$ MIoU loss.
