SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
This enables more efficient deployment of neural networks on resource-constrained hardware like autonomous cars and FPGAs, representing a novel method for a known bottleneck.
The paper tackled the problem of reducing the size of deep neural networks while maintaining accuracy, achieving AlexNet-level accuracy on ImageNet with 50x fewer parameters and compressing the model to less than 0.5MB.
Recent research on deep neural networks has focused primarily on improving accuracy. For a given accuracy level, it is typically possible to identify multiple DNN architectures that achieve that accuracy level. With equivalent accuracy, smaller DNN architectures offer at least three advantages: (1) Smaller DNNs require less communication across servers during distributed training. (2) Smaller DNNs require less bandwidth to export a new model from the cloud to an autonomous car. (3) Smaller DNNs are more feasible to deploy on FPGAs and other hardware with limited memory. To provide all of these advantages, we propose a small DNN architecture called SqueezeNet. SqueezeNet achieves AlexNet-level accuracy on ImageNet with 50x fewer parameters. Additionally, with model compression techniques we are able to compress SqueezeNet to less than 0.5MB (510x smaller than AlexNet). The SqueezeNet architecture is available for download here: https://github.com/DeepScale/SqueezeNet