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Recent research on deep neural networks has focused primarily on improvingaccuracy. For a given accuracy level, it is typically possible to identifymultiple DNN architectures that achieve that accuracy level. With equivalentaccuracy, smaller DNN architectures offer at least three advantages: (1)Smaller DNNs require less communication across servers during distributedtraining. (2) Smaller DNNs require less bandwidth to export a new model fromthe cloud to an autonomous car. (3) Smaller DNNs are more feasible to deploy onFPGAs and other hardware with limited memory. To provide all of theseadvantages, we propose a small DNN architecture called SqueezeNet. SqueezeNetachieves AlexNet-level accuracy on ImageNet with 50x fewer parameters.Additionally, with model compression techniques we are able to compressSqueezeNet to less than 0.5MB (510x smaller than AlexNet).  The SqueezeNet architecture is available for download here:https://github.com/DeepScale/SqueezeNet

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size