Novel computing architectures based on resistive switching memories (also known as
memristors or RRAMs) have been shown to be promising approaches for tackling the …

Recent years have seen a rapid rise of artificial neural networks being employed in a
number of cognitive tasks. The ever‐increasing computing requirements of these structures …

The conventional computing system with the architecture of von Neumann has greatly
benefited our humans for past decades, while it is also suffered from low efficiency due to …

Brain-inspired neuromorphic computing has emerged as a promising solution to overcome
the energy and speed limitations of conventional von Neumann architectures. In this context …

Memristor neural networks are increasingly recognized for their suitability in matrix
operations and low power consumption, offering a promising solution to overcome the …

Abstract Memristor Neural Networks (MNNs) stand out for their low power consumption and
accelerated matrix operations, making them a promising hardware solution for neural …

Memristor and its crossbar structure have been widely studied and proven to be naturally
suitable for implementing vector-matrix multiplier (VMM) operation in neural networks …

Neural state machines (NSMs) with weight tunable synapses and leaky integrate-and-fire
neurons can control the workflow according to the input information and current state, which …

Memristors show great promise in neuromorphic computing owing to their high-density
integration, fast computing and low-energy consumption. However, the non-ideal update of …

Memristive neural network (MNN) has emerged as a new computing architecture with high
speed and low power consumption, but its hardware implementation is hampered mainly by …