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Found 15 papers, 5 papers with code
Performance Evaluation of Neuromorphic Hardware for Onboard Satellite Communication Applications
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
The latest satellite communication (SatCom) missions are characterized by a fully reconfigurable on-board software-defined payload, capable of adapting radio resources to the temporal and spatial variations of the system traffic.
Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity
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 for reliable AI systems.
Neuromorphic Integrated Sensing and Communications
Neuromorphic computing is an emerging technology that support event-driven data processing for applications requiring efficient online inference and/or control.
Bayesian Continual Learning via Spiking Neural Networks
In this paper, we take steps towards the design of neuromorphic systems that are capable of adaptation to changing learning tasks, while producing well-calibrated uncertainty quantification estimates.
Neuromorphic Wireless Cognition: Event-Driven Semantic Communications for Remote Inference
In order to enable adaptation of the receiver to the fading channel conditions, we introduce a hypernetwork to control the weights of the decoding SNN using pilots.
Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck
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 images for generative models, and need to be encoded into spikes.
BiSNN: Training Spiking Neural Networks with Binary Weights via Bayesian Learning
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 need to perform multiplications.
Spiking Neural Networks -- Part I: Detecting Spatial Patterns
Then, we review learning algorithms and applications for SNNs that aim at mimicking the functionality of ANNs by detecting or generating spatial patterns in rate-encoded spiking signals.
Spiking Neural Networks -- Part II: Detecting Spatio-Temporal Patterns
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 III: Neuromorphic Communications
Synergies between wireless communications and artificial intelligence are increasingly motivating research at the intersection of the two fields.
End-to-End Learning of Neuromorphic Wireless Systems for Low-Power Edge Artificial Intelligence
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 (SNNs).
VOWEL: A Local Online Learning Rule for Recurrent Networks of Probabilistic Spiking Winner-Take-All Circuits
Networks of spiking neurons and Winner-Take-All spiking circuits (WTA-SNNs) can detect information encoded in spatio-temporal multi-valued events.
Federated Neuromorphic Learning of Spiking Neural Networks for Low-Power Edge Intelligence
To this end, we introduce an online FL-based learning rule for networked on-device SNNs, which we refer to as FL-SNN.
Optimizing Pipelined Computation and Communication for Latency-Constrained Edge Learning
Consider a device that is connected to an edge processor via a communication channel.
