Structured Convolution Matrices for Energy-efficient Deep learning
  We derive a relationship between network representation in energy-efficient neuromorphic architectures and block Toplitz convolutional matrices. Inspired by this connection, we develop deep convolutional networks using a family of structured convolutional matrices and achieve state-of-the-art trade-off between energy efficiency and classification accuracy for well-known image recognition tasks. We also put forward a novel method to train binary convolutional networks by utilising an existing connection between noisy-rectified linear units and binary activations. 
Rathinakumar Appuswamy, Tapan Nayak, John Arthur, Steven Esser, Paul   Merolla, Jeffrey Mckinstry, Timothy Melano, Myron Fli...
cs.NE cs.AI cs.CV cs.LG