Low Precision Neural Networks using Subband Decomposition

Understand

Large-scale deep neural networks (DNN) have been successfully used in a number of tasks from image recognition to natural language processing.

• They are trained using large training sets on large models, making them computationally and memory intensive.
• As such, there is much interest in research development for faster training and test time.
• In this paper, we present a unique approach using lower precision weights for more efficient and faster training phase.