Memristive technology has been rapidly emerging as a potential alternative to traditional
CMOS technology, which is facing fundamental limitations in its development. Since oxide …

The rapid development of artificial intelligence (AI) demands the rapid development of
domain-specific hardware specifically designed for AI applications. Neuro-inspired …

Learning is highly important for edge intelligence devices to adapt to different application
scenes and owners. Current technologies for training neural networks require moving …

Modern computation based on von Neumann architecture is now a mature cutting-edge
science. In the von Neumann architecture, processing and memory units are implemented …

Recently, the increasing demand for data-centric applications is driving the elimination of
image sensing, memory and computing unit interface, thus promising for latency-and energy …

Compute-in-memory (CIM) is a new computing paradigm that addresses the memory-wall
problem in hardware accelerator design for deep learning. The input vector and weight …

The rapid development of neuro-inspired computing demands synaptic devices with ultrafast
speed, low power consumption, and multiple non-volatile states, among other features …

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

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

Memristor crossbar with programmable conductance could overcome the energy
consumption and speed limitations of neural networks when executing core computing tasks …