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Deep neural networks are state of the art methods for many learning tasks dueto their ability to extract increasingly better features at each network layer.However, the improved performance of additional layers in a deep network comesat the cost of added latency and energy usage in feedforward inference. Asnetworks continue to get deeper and larger, these costs become more prohibitivefor real-time and energy-sensitive applications. To address this issue, wepresent BranchyNet, a novel deep network architecture that is augmented withadditional side branch classifiers. The architecture allows prediction resultsfor a large portion of test samples to exit the network early via thesebranches when samples can already be inferred with high confidence. BranchyNetexploits the observation that features learned at an early layer of a networkmay often be sufficient for the classification of many data points. For moredifficult samples, which are expected less frequently, BranchyNet will usefurther or all network layers to provide the best likelihood of correctprediction. We study the BranchyNet architecture using several well-knownnetworks (LeNet, AlexNet, ResNet) and datasets (MNIST, CIFAR10) and show thatit can both improve accuracy and significantly reduce the inference time of thenetwork.

BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks