  This work proposes an algorithm, called NetAdapt, that automatically adapts a
pre-trained deep neural network to a mobile platform given a resource budget.
While many existing algorithms simplify networks based on the number of MACs or
weights, optimizing those indirect metrics may not necessarily reduce the
direct metrics, such as latency and energy consumption. To solve this problem,
NetAdapt incorporates direct metrics into its adaptation algorithm. These
direct metrics are evaluated using empirical measurements, so that detailed
knowledge of the platform and toolchain is not required. NetAdapt automatically
and progressively simplifies a pre-trained network until the resource budget is
met while maximizing the accuracy. Experiment results show that NetAdapt
achieves better accuracy versus latency trade-offs on both mobile CPU and
mobile GPU, compared with the state-of-the-art automated network simplification
algorithms. For image classification on the ImageNet dataset, NetAdapt achieves
up to a 1.7$\times$ speedup in measured inference latency with equal or higher
accuracy on MobileNets (V1&V2).
