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Spiking Generative Adversarial Networks With a Neural Network Discriminator: Local Training, Bayesian Models, and Continual Meta-Learning
Bleema Rosenfeld, Osvaldo Simeone, Bipin Rajendran
Neuromorphic data carries information in spatio-temporal patterns encoded by spikes. Accordingly, a central problem in neuromorphic computing is training spiking neural networks (S…
Fast On-Device Adaptation for Spiking Neural Networks via Online-Within-Online Meta-Learning
Bleema Rosenfeld, Bipin Rajendran, Osvaldo Simeone
Spiking Neural Networks (SNNs) have recently gained popularity as machine learning models for on-device edge intelligence for applications such as mobile healthcare management and…
Hybrid In-memory Computing Architecture for the Training of Deep Neural Networks
Vinay Joshi, Wangxin He, Jae-sun Seo +1
The cost involved in training deep neural networks (DNNs) on von-Neumann architectures has motivated the development of novel solutions for efficient DNN training accelerators. We…