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.

提出了一种使用策略网络进行网络嵌入的强化学习的资源有效型神经架构搜索方法，可以在计算资源受限制的情况下获得高性能的神经网络结构，并在图像识别和关键词识别问题上实现了先进的性能。