805 citations · 810 across the 3 of their papers we have counts for
5 papers
Spiking Optical Flow for Event-based Sensors Using IBM's TrueNorth Neurosynaptic System
Germain Haessig, Andrew Cassidy, Rodrigo Alvarez +2
This paper describes a fully spike-based neural network for optical flow estimation from Dynamic Vision Sensor data. A low power embedded implementation of the method which combine…
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…
Conversion of Artificial Recurrent Neural Networks to Spiking Neural Networks for Low-power Neuromorphic Hardware
Peter U. Diehl, Guido Zarrella, Andrew Cassidy +2
In recent years the field of neuromorphic low-power systems that consume orders of magnitude less power gained significant momentum. However, their wider use is still hindered by t…
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…
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…