A spiking neural network (SNN) inspired by the structure and principles of the human brain
can significantly enhance the energy efficiency of artificial intelligence computing by …

Traditional von Neumann computing architecture with separated computation and storage
units has already impeded the data processing performance and energy efficiency, calling …

Cointegration of multistate single-transistor neurons and synapses was demonstrated for
highly scalable neuromorphic hardware, using nanoscale complementary metal-oxide …

Neuromorphic or “brain-like” computation is a leading candidate for efficient, fault-tolerant
processing of large-scale data as well as real-time sensing and transduction of complex …

Artificial intelligence is a promising concept in modern and future societies. Presently,
software programs are used but with a bulky computer size and large power consumption …

Spintronic devices are considered as promising candidates in implementing neuromorphic
systems or hardware neural networks, which are expected to perform better than other …

Artificial intelligence (AI) and blockchain technology have emerged as increasingly
prevalent and influential elements shaping global trends in Information and …

Conductive bridging random access memory (CBRAM) has been considered to be a
promising emerging device for artificial synapses in neuromorphic computing systems. Good …

In this paper, we demonstrate the linear conductance-change characteristics of a conductive-
bridging RAM (CBRAM) to be employed as an artificial synapse device in neuromorphic …

The potential of resistive switching (RS) devices based on TiN/Ti/HfO 2/W stacks to mimic
synapses within a neuromorphic applications context is analyzed in depth. The fabrication …