Structured Convolution Matrices for Energy-efficient Deep learning

no code implementations • 8 Jun 2016 • Rathinakumar Appuswamy, Tapan Nayak, John Arthur, Steven Esser, Paul Merolla, Jeffrey Mckinstry, Timothy Melano, Myron Flickner, Dharmendra Modha

We derive a relationship between network representation in energy-efficient neuromorphic architectures and block Toplitz convolutional matrices.

General Classification

Deep neural networks are robust to weight binarization and other non-linear distortions

no code implementations • 7 Jun 2016 • Paul Merolla, Rathinakumar Appuswamy, John Arthur, Steve K. Esser, Dharmendra Modha

Recent results show that deep neural networks achieve excellent performance even when, during training, weights are quantized and projected to a binary representation.

Binarization Data Augmentation +1

Gibbs Sampling with Low-Power Spiking Digital Neurons

no code implementations • 26 Mar 2015 • Srinjoy Das, Bruno Umbria Pedroni, Paul Merolla, John Arthur, Andrew S. Cassidy, Bryan L. Jackson, Dharmendra Modha, Gert Cauwenberghs, Ken Kreutz-Delgado

Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in a wide variety of applications including image classification and speech recognition.

General Classification Image Classification +2