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Designing accurate and efficient ConvNets for mobile devices is challengingbecause the design space is combinatorially large. Due to this, previous neuralarchitecture search (NAS) methods are computationally expensive. ConvNetarchitecture optimality depends on factors such as input resolution and targetdevices. However, existing approaches are too expensive for case-by-caseredesigns. Also, previous work focuses primarily on reducing FLOPs, but FLOPcount does not always reflect actual latency. To address these, we propose adifferentiable neural architecture search (DNAS) framework that usesgradient-based methods to optimize ConvNet architectures, avoiding enumeratingand training individual architectures separately as in previous methods.FBNets, a family of models discovered by DNAS surpass state-of-the-art modelsboth designed manually and generated automatically. FBNet-B achieves 74.1%top-1 accuracy on ImageNet with 295M FLOPs and 23.1 ms latency on a Samsung S8phone, 2.4x smaller and 1.5x faster than MobileNetV2-1.3 with similar accuracy.Despite higher accuracy and lower latency than MnasNet, we estimate FBNet-B'ssearch cost is 420x smaller than MnasNet's, at only 216 GPU-hours. Searched fordifferent resolutions and channel sizes, FBNets achieve 1.5% to 6.4% higheraccuracy than MobileNetV2. The smallest FBNet achieves 50.2% accuracy and 2.9ms latency (345 frames per second) on a Samsung S8. Over a Samsung-optimizedFBNet, the iPhone-X-optimized model achieves a 1.4x speedup on an iPhone X.

FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search