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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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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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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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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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Low-Power Neuromorphic Hardware for Signal Processing Applications
Machine learning has emerged as the dominant tool for implementing compl...
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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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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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Adversarial Training for Probabilistic Spiking Neural Networks
Classifiers trained using conventional empirical risk minimization or ma...
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Stochastic Deep Learning in Memristive Networks
We study the performance of stochastically trained deep neural networks ...
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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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Training Probabilistic Spiking Neural Networks with First-to-spike Decoding
Third-generation neural networks, or Spiking Neural Networks (SNNs), aim...
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