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08/22/2023
Energy-Efficient On-Board Radio Resource Management for Satellite Communications via Neuromorphic Computing
The latest satellite communication (SatCom) missions are characterized b...
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04/21/2023
A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Interfaces
Brain-computer interfaces are being explored for a wide variety of thera...
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02/02/2023
Bayesian Inference on Binary Spiking Networks Leveraging Nanoscale Device Stochasticity
Bayesian Neural Networks (BNNs) can overcome the problem of overconfiden...
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11/02/2021
Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning
Neuromorphic data carries information in spatio-temporal patterns encode...
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02/21/2021
Fast On-Device Adaptation for Spiking Neural Networks via Online-Within-Online Meta-Learning
Spiking Neural Networks (SNNs) have recently gained popularity as machin...
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02/10/2021
Hybrid In-memory Computing Architecture for the Training of Deep Neural Networks
The cost involved in training deep neural networks (DNNs) on von-Neumann...
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08/05/2020
SpinAPS: A High-Performance Spintronic Accelerator for Probabilistic Spiking Neural Networks
We discuss a high-performance and high-throughput hardware accelerator f...
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04/30/2020
Memristors – from In-memory computing, Deep Learning Acceleration, Spiking Neural Networks, to the Future of Neuromorphic and Bio-inspired Computing
Machine learning, particularly in the form of deep learning, has driven ...
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03/25/2020
ESSOP: Efficient and Scalable Stochastic Outer Product Architecture for Deep Learning
Deep neural networks (DNNs) have surpassed human-level accuracy in a var...
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05/28/2019
Supervised Learning in Spiking Neural Networks with Phase-Change Memory Synapses
Spiking neural networks (SNN) are artificial computational models that h...
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01/11/2019
Low-Power Neuromorphic Hardware for Signal Processing Applications
Machine learning has emerged as the dominant tool for implementing compl...
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10/23/2018
Training Multilayer Spiking Neural Networks using NormAD based Spatio-Temporal Error Backpropagation
Spiking neural networks (SNNs) have garnered a great amount of interest ...
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10/23/2018
Learning First-to-Spike Policies for Neuromorphic Control Using Policy Gradients
Artificial Neural Networks (ANNs) are currently being used as function a...
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02/22/2018
Adversarial Training for Probabilistic Spiking Neural Networks
Classifiers trained using conventional empirical risk minimization or ma...
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11/09/2017
Stochastic Deep Learning in Memristive Networks
We study the performance of stochastically trained deep neural networks ...
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11/09/2017
Learning and Real-time Classification of Hand-written Digits With Spiking Neural Networks
We describe a novel spiking neural network (SNN) for automated, real-tim...
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10/29/2017
