Memristors and memristor crossbar arrays have been widely studied for neuromorphic and
other in-memory computing applications. To achieve optimal system performance, however …

As the limits of transistor technology are approached, feature size in integrated circuit
transistors has been reduced very near to the minimum physically-realizable channel length …

By mimicking the highly parallel biological systems, neuromorphic hardware provides the
capability of information processing within a compact and energy-efficient platform …

We propose a hardware-friendly architecture of a convolutional neural network using a 32×
32 memristor crossbar array having an overshoot suppression layer. The gradual switching …

Using memristor crossbar arrays to accelerate computations is a promising approach to
efficiently implement algorithms in deep neural networks. Early demonstrations, however …

Memristor crossbar arrays carry out multiply-add operations in parallel in the analog domain,
and so can enable neuromorphic systems with high throughput at low energy and area …

Brain‐inspired neuromorphic computing is a new paradigm that holds great potential to
overcome the intrinsic energy and speed issues of traditional von Neumann based …

The memristor is a resistive switch where its resistive state is programable based on the
applied voltage or current. Memristive devices are thus capable of storing and computing …

Memristors have recently attracted significant interest due to their applicability as promising
building blocks of neuromorphic computing and electronic systems. The dynamic …

Memristors are memory resistors that promise the efficient implementation of synaptic
weights in artificial neural networks. Whereas demonstrations of the synaptic operation of …