Compression of Deep Neural Networks on the Fly
This addresses the challenge of deploying deep neural networks on resource-limited devices like smartphones, though it appears incremental as it builds on existing compression techniques.
The paper tackles the problem of deep neural networks being too large for embedded devices by introducing a compression method applied during training, achieving significantly larger compression rates than state-of-the-art methods on MNIST and CIFAR10 datasets.
Thanks to their state-of-the-art performance, deep neural networks are increasingly used for object recognition. To achieve these results, they use millions of parameters to be trained. However, when targeting embedded applications the size of these models becomes problematic. As a consequence, their usage on smartphones or other resource limited devices is prohibited. In this paper we introduce a novel compression method for deep neural networks that is performed during the learning phase. It consists in adding an extra regularization term to the cost function of fully-connected layers. We combine this method with Product Quantization (PQ) of the trained weights for higher savings in storage consumption. We evaluate our method on two data sets (MNIST and CIFAR10), on which we achieve significantly larger compression rates than state-of-the-art methods.