805 citations · 924 across the 6 of their papers we have counts for
7 papers
Structured Convolution Matrices for Energy-efficient Deep learning
Rathinakumar Appuswamy, Tapan Nayak, John Arthur +6
We derive a relationship between network representation in energy-efficient neuromorphic architectures and block Toplitz convolutional matrices. Inspired by this connection, we dev…
Deep neural networks are robust to weight binarization and other non-linear distortions
Paul Merolla, Rathinakumar Appuswamy, John Arthur +2
Recent results show that deep neural networks achieve excellent performance even when, during training, weights are quantized and projected to a binary representation. Here, we sho…
Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing
Steven K. Esser, Paul A. Merolla, John V. Arthur +13
Deep networks are now able to achieve human-level performance on a broad spectrum of recognition tasks. Independently, neuromorphic computing has now demonstrated unprecedented ene…
TrueHappiness: Neuromorphic Emotion Recognition on TrueNorth
Peter U. Diehl, Bruno U. Pedroni, Andrew Cassidy +3
We present an approach to constructing a neuromorphic device that responds to language input by producing neuron spikes in proportion to the strength of the appropriate positive or…
Mapping Generative Models onto a Network of Digital Spiking Neurons
Bruno U. Pedroni, Srinjoy Das, John V. Arthur +5
Stochastic neural networks such as Restricted Boltzmann Machines (RBMs) have been successfully used in applications ranging from speech recognition to image classification. Inferen…
Gibbs Sampling with Low-Power Spiking Digital Neurons
Srinjoy Das, Bruno Umbria Pedroni, Paul Merolla +6
Restricted Boltzmann Machines and Deep Belief Networks have been successfully used in a wide variety of applications including image classification and speech recognition. Inferenc…