172 citations · 302 across the 7 of their papers we have counts for
5 papers · 1 filter
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…