  Neural Architecture Search (NAS) is a laborious process. Prior work on
automated NAS targets mainly on improving accuracy, but lacks consideration of
computational resource use. We propose the Resource-Efficient Neural Architect
(RENA), an efficient resource-constrained NAS using reinforcement learning with
network embedding. RENA uses a policy network to process the network embeddings
to generate new configurations. We demonstrate RENA on image recognition and
keyword spotting (KWS) problems. RENA can find novel architectures that achieve
high performance even with tight resource constraints. For CIFAR10, it achieves
2.95% test error when compute intensity is greater than 100 FLOPs/byte, and
3.87% test error when model size is less than 3M parameters. For Google Speech
Commands Dataset, RENA achieves the state-of-the-art accuracy without resource
constraints, and it outperforms the optimized architectures with tight resource
constraints.
