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Clip: Published as a conference paper at ICLR 2019 PROXYLESSNAS: DIRECTNEURALARCHITECTURE SEARCH ONTARGETTASK ANDHARDWARE Han Cai, Ligeng Zhu, Song Han Massachusetts Institute of Technology fhancai, ligeng, songhan g@mit.edu ABSTRACT Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms (e.g.10 4 GPU hours) makes it difcult todirectlysearch the architectures on large-scale tasks (e.g. ImageNet). Differen- tiable NAS can reduce the cost of GPU hours via a continuous representation of network architecture but suffers from the high GPU memory consumption issue

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