The information technologies have been increasing exponentially following Moore's law
over the past decades. This has fundamentally changed the ways of work and life. However …

In this paper, we reviewed the recent trends on neuromorphic computing using emerging
memory technologies. Two representative learning algorithms used to implement a …

Resistive random access memory (ReRAM) is often considered as a strong candidate for
storing the weights in non-von Neumann neuromorphic computing systems. This paper …

In traditional von Neumann computing architectures, the essential transfer of data between
the processor and memory hierarchies limits the computational efficiency of next-generation …

The article discusses the modeling of interconnected memory elements within a
neuromorphic network. A mathematical model is proposed that describes a hardware …

Based on the inherent stochasticity of CMOS-integrated HfO 2-based resistive random
access memory (RRAM) devices, a new learning algorithm for neuro-morphic systems is …

Continual learning is the ability to acquire a new task or knowledge without losing any
previously collected information. Achieving continual learning in artificial intelligence (AI) is …

Brain-inspired neuromorphic engineering aims at designing networks capable of learning
from their own experience, in terms of both plasticity and stability. In biology, homeostatic …

In this work, we present the first implementation of spike-timing-dependent plasticity (STDP)
and unsupervised learning in a mainstream NOR Flash memory array based on floating …

Resistive switching memory (RRAM) devices have been proposed to boost the density and
the bio-realistic plasticity in neural networks. One of the main limitations to the development …