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Neural architecture search (NAS) has a great impact by automaticallydesigning effective neural network architectures. However, the prohibitivecomputational demand of conventional NAS algorithms (e.g. $10^4$ GPU hours)makes it difficult to \emph{directly} search the architectures on large-scaletasks (e.g. ImageNet). Differentiable NAS can reduce the cost of GPU hours viaa continuous representation of network architecture but suffers from the highGPU memory consumption issue (grow linearly w.r.t. candidate set size). As aresult, they need to utilize~\emph{proxy} tasks, such as training on a smallerdataset, or learning with only a few blocks, or training just for a few epochs.These architectures optimized on proxy tasks are not guaranteed to be optimalon the target task. In this paper, we present \emph{ProxylessNAS} that can\emph{directly} learn the architectures for large-scale target tasks and targethardware platforms. We address the high memory consumption issue ofdifferentiable NAS and reduce the computational cost (GPU hours and GPU memory)to the same level of regular training while still allowing a large candidateset. Experiments on CIFAR-10 and ImageNet demonstrate the effectiveness ofdirectness and specialization. On CIFAR-10, our model achieves 2.08\% testerror with only 5.7M parameters, better than the previous state-of-the-artarchitecture AmoebaNet-B, while using 6$\times$ fewer parameters. On ImageNet,our model achieves 3.1\% better top-1 accuracy than MobileNetV2, while being1.2$\times$ faster with measured GPU latency. We also apply ProxylessNAS tospecialize neural architectures for hardware with direct hardware metrics (e.g.latency) and provide insights for efficient CNN architecture design.

ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware