Resistive memories are outstanding electron devices that have displayed a large potential in
a plethora of applications such as nonvolatile data storage, neuromorphic computing …

The von Neumann bottleneck and memory wall have posed fundamental limitations in
latency and energy consumption of modern computers based on von Neumann architecture …

Brain-inspired computing enabled by memristors has gained prominence over the years due
to the nanoscale footprint and reduced complexity for implementing synapses and neurons …

Neuromorphic computing, inspired by the biological neuronal system, is a high potential
approach to substantially alleviate the cost of computational latency and energy for massive …

Bio-inspired transistor synapses use solid electrolytes to achieve low-power operation and
rich synaptic behaviors via ion diffusion and trap**. While these neuromorphic devices …

Low energy consumption per synaptic event is important for artificial synapses in
applications of highly integrated and large-scale neuromorphic computing systems …

The neuromorphic computing paradigm, characterized by nonvolatile artificial synapses, is
seen as crucial for overcoming the limitations of the von Neumann architecture. Moreover …

In today's world, the rise of big data demands a new computing paradigm beyond the von
Neumann architecture to handle massive datasets effectively. Neuromorphic computing …

Resistive memories are outstanding electron devices that have displayed a large potential in
a plethora of applications such as nonvolatile data storage, neuro-morphic computing …

In today's world, the rise of big data demands a new computing paradigm beyond the von
Neumann architecture to handle massive datasets effectively. Neuromorphic computing …