8 citations · 23 across the 13 of their papers we have counts for
15 papers
Performance Evaluation of Neuromorphic Hardware for Onboard Satellite Communication Applications
Eva Lagunas, Flor Ortiz, Geoffrey Eappen +7
Spiking neural networks (SNNs) implemented on neuromorphic processors (NPs) can enhance the energy efficiency of deployments of artificial intelligence (AI) for specific workloads.…
Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing
Flor Ortiz, Nicolas Skatchkovsky, Eva Lagunas +6
The latest satellite communication (SatCom) missions are characterized by a fully reconfigurable on-board software-defined payload, capable of adapting radio resources to the tempo…
Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity
Prabodh Katti, Nicolas Skatchkovsky, Osvaldo Simeone +2
Bayesian Neural Networks (BNNs) can overcome the problem of overconfidence that plagues traditional frequentist deep neural networks, and are hence considered to be a key enabler f…
Neuromorphic Integrated Sensing and Communications
Jiechen Chen, Nicolas Skatchkovsky, Osvaldo Simeone
Neuromorphic computing is an emerging technology that support event-driven data processing for applications requiring efficient online inference and/or control. Recent work has int…
Bayesian Continual Learning via Spiking Neural Networks
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
Among the main features of biological intelligence are energy efficiency, capacity for continual adaptation, and risk management via uncertainty quantification. Neuromorphic engine…
Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference
Jiechen Chen, Nicolas Skatchkovsky, Osvaldo Simeone
Neuromorphic computing is an emerging computing paradigm that moves away from batched processing towards the online, event-driven, processing of streaming data. Neuromorphic chips,…
Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck
Nicolas Skatchkovsky, Osvaldo Simeone, Hyeryung Jang
One of the key challenges in training Spiking Neural Networks (SNNs) is that target outputs typically come in the form of natural signals, such as labels for classification or imag…
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…
Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence
Nicolas Skatchkovsky, Hyeryung Jang, Osvaldo Simeone
Spiking Neural Networks (SNNs) offer a promising alternative to conventional Artificial Neural Networks (ANNs) for the implementation of on-device low-power online learning and inf…
Optimizing Pipelined Computation and Communication for Latency-Constrained Edge Learning
Nicolas Skatchkovsky, Osvaldo Simeone
Consider a device that is connected to an edge processor via a communication channel. The device holds local data that is to be offloaded to the edge processor so as to train a mac…