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Large-scale deep neural networks (DNN) have been successfully used in anumber of tasks from image recognition to natural language processing. They aretrained using large training sets on large models, making them computationallyand memory intensive. As such, there is much interest in research developmentfor faster training and test time. In this paper, we present a unique approachusing lower precision weights for more efficient and faster training phase. Weseparate imagery into different frequency bands (e.g. with differentinformation content) such that the neural net can better learn using less bits.We present this approach as a complement existing methods such as pruningnetwork connections and encoding learning weights. We show results where thisapproach supports more stable learning with 2-4X reduction in precision with17X reduction in DNN parameters.

Low Precision Neural Networks using Subband Decomposition