172 citations · 319 across the 10 of their papers we have counts for
8 papers · 1 filter
Continuous Learning in a Single-Incremental-Task Scenario with Spike Features
Ruthvik Vaila, John Chiasson, Vishal Saxena
Deep Neural Networks (DNNs) have two key deficiencies, their dependence on high precision computing and their inability to perform sequential learning, that is, when a DNN is train…
A Deep Unsupervised Feature Learning Spiking Neural Network with Binarized Classification Layers for EMNIST Classification using SpykeFlow
Ruthvik Vaila, John Chiasson, Vishal Saxena
End user AI is trained on large server farms with data collected from the users. With ever increasing demand for IOT devices, there is a need for deep learning approaches that can…
Deep Convolutional Spiking Neural Networks for Image Classification
Ruthvik Vaila, John Chiasson, Vishal Saxena
Spiking neural networks are biologically plausible counterparts of the artificial neural networks, artificial neural networks are usually trained with stochastic gradient descent a…
Energy-Efficient CMOS Memristive Synapses for Mixed-Signal Neuromorphic System-on-a-Chip
Vishal Saxena, Xinyu Wu, Kehan Zhu
Emerging non-volatile memory (NVM), or memristive, devices promise energy-efficient realization of deep learning, when efficiently integrated with mixed-signal integrated circuits…
Dendritic-Inspired Processing Enables Bio-Plausible STDP in Compound Binary Synapses
Xinyu Wu, Vishal Saxena
Brain-inspired learning mechanisms, e.g. spike timing dependent plasticity (STDP), enable agile and fast on-the-fly adaptation capability in a spiking neural network. When incorpor…
Homogeneous Spiking Neuromorphic System for Real-World Pattern Recognition
Xinyu Wu, Vishal Saxena, Kehan Zhu
A neuromorphic chip that combines CMOS analog spiking neurons and memristive synapses offers a promising solution to brain-inspired computing, as it can provide massive neural netw…
A CMOS Spiking Neuron for Dense Memristor-Synapse Connectivity for Brain-Inspired Computing
Xinyu Wu, Vishal Saxena, Kehan Zhu
Neuromorphic systems that densely integrate CMOS spiking neurons and nano-scale memristor synapses open a new avenue of brain-inspired computing. Existing silicon neurons have mold…
A CMOS Spiking Neuron for Brain-Inspired Neural Networks with Resistive Synapses and In-Situ Learning
Xinyu Wu, Vishal Saxena, Kehan Zhu +1
Nanoscale resistive memories are expected to fuel dense integration of electronic synapses for large-scale neuromorphic system. To realize such a brain-inspired computing chip, a c…