Structured Convolution Matrices for Energy-efficient Deep learning

Understand

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.