Sigma Delta Quantized Networks

How much the eye tells the brain

Kristin Koch, Judith McLean, Ronen Segev, Michael A Freed, Michael J Berry, Vijay Balasubramanian, and Peter Sterling · 2006

Earlier work this paper cites.

A 128 × \times 128 120 db 15 μ \mu s latency asynchronous temporal contrast vision sensor

Patrick Lichtsteiner, Christoph Posch, and Tobi Delbruck · 2008

Earlier work this paper cites.

Herding dynamical weights to learn

Max Welling · 2009

Earlier work this paper cites.

Understanding the difficulty of training deep feedforward neural networks

Xavier Glorot and Yoshua Bengio · 2010

Earlier work this paper cites.

Neural networks for machine learning. coursera, video lectures

Geoffrey Hinton · 2012

Earlier work this paper cites.

1.1 computing’s energy problem (and what we can do about it)

Mark Horowitz · 2014

Cited alongside, same era.

Very deep convolutional networks for large-scale image recognition

Original

Karen Simonyan and Andrew Zisserman · 2014

Cited alongside, same era.

Vlsi implementation of deep neural network using integral stochastic computing

Original

Arash Ardakani, François Leduc-Primeau, Naoya Onizawa, Takahiro Hanyu, and Warren J Gross · 2015

Cited alongside, same era.

Fast-classifying, high-accuracy spiking deep networks through weight and threshold balancing

Peter U Diehl, Daniel Neil, Jonathan Binas, Matthew Cook, Shih-Chii Liu, and Michael Pfeiffer · 2015

Cited alongside, same era.

ImageNet Large Scale Visual Recognition Challenge

Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015

Cited alongside, same era.

Binarized neural networks: Training neural networks with weights and activations constrained to+ 1 or-

Matthieu Courbariaux, Itay Hubara, COM Daniel Soudry, Ran El-Yaniv, and Yoshua Bengio

Cited in the paper.

Convolutional networks for fast, energy-efficient neuromorphic computing

Original

Steven K Esser, Paul A Merolla, John V Arthur, Andrew S Cassidy, Rathinakumar Appuswamy, Alexander Andreopoulos, David J Berg, Jeffrey L McKinstry, Timothy Melano, Davis R Barch, et al · 2016

Closest in time.

Training deep spiking neural networks using backpropagation

Original

Jun Haeng Lee, Tobi Delbruck, and Michael Pfeiffer · 2016

Closest in time.

Deep spiking networks

Original

Peter O’Connor and Max Welling · 2016

Closest in time.

Lif and simplified srm neurons encode signals into spikes via a form of asynchronous pulse sigma-delta modulation

Young C Yoon · 2016

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Fast and efficient asynchronous neural computation with adapting spiking neural networks