8 citations · 23 across the 13 of their papers we have counts for
6 papers · 1 filter
BiSNN: Training Spiking Neural Networks with Binary Weights via Bayesian Learning
Hyeryung Jang, Nicolas Skatchkovsky, Osvaldo Simeone
Artificial Neural Network (ANN)-based inference on battery-powered devices can be made more energy-efficient by restricting the synaptic weights to be binary, hence eliminating the…
Spiking Neural Networks -- Part III: Neuromorphic Communications
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
Synergies between wireless communications and artificial intelligence are increasingly motivating research at the intersection of the two fields. On the one hand, the presence of m…
Spiking Neural Networks -- Part II: Detecting Spatio-Temporal Patterns
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
Inspired by the operation of biological brains, Spiking Neural Networks (SNNs) have the unique ability to detect information encoded in spatio-temporal patterns of spiking signals.…
Spiking Neural Networks -- Part I: Detecting Spatial Patterns
Hyeryung Jang, Nicolas Skatchkovsky, Osvaldo Simeone
Spiking Neural Networks (SNNs) are biologically inspired machine learning models that build on dynamic neuronal models processing binary and sparse spiking signals in an event-driv…
End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
This paper introduces a novel "all-spike" low-power solution for remote wireless inference that is based on neuromorphic sensing, Impulse Radio (IR), and Spiking Neural Networks (S…
VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner-Take-All Circuits
Hyeryung Jang, Nicolas Skatchkovsky, Osvaldo Simeone
Networks of spiking neurons and Winner-Take-All spiking circuits (WTA-SNNs) can detect information encoded in spatio-temporal multi-valued events. These are described by the timing…