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Differentiable Neural Architecture Search (DNAS) has demonstrated greatsuccess in designing state-of-the-art, efficient neural networks. However,DARTS-based DNAS's search space is small when compared to other searchmethods', since all candidate network layers must be explicitly instantiated inmemory. To address this bottleneck, we propose a memory and computationallyefficient DNAS variant: DMaskingNAS. This algorithm expands the search space byup to $10^{14}\times$ over conventional DNAS, supporting searches over spatialand channel dimensions that are otherwise prohibitively expensive: inputresolution and number of filters. We propose a masking mechanism for featuremap reuse, so that memory and computational costs stay nearly constant as thesearch space expands. Furthermore, we employ effective shape propagation tomaximize per-FLOP or per-parameter accuracy. The searched FBNetV2s yieldstate-of-the-art performance when compared with all previous architectures.With up to 421$\times$ less search cost, DMaskingNAS finds models with 0.9%higher accuracy, 15% fewer FLOPs than MobileNetV3-Small; and with similaraccuracy but 20% fewer FLOPs than Efficient-B0. Furthermore, our FBNetV2outperforms MobileNetV3 by 2.6% in accuracy, with equivalent model size.FBNetV2 models are open-sourced athttps://github.com/facebookresearch/mobile-vision.

FBNetV2: Differentiable Neural Architecture Search for Spatial and Channel Dimensions