In-memory computing (IMC) has emerged as a new computing paradigm able to alleviate or
suppress the memory bottleneck, which is the major concern for energy efficiency and …

Deep Neural Networks (DNNs) have shown significant advantages in many domains, such
as pattern recognition, prediction, and control optimization. The edge computing demand in …

Memristor based neuromorphic computing systems give alternative solutions to boost the
computing energy efficiency of Neural Network (NN) algorithms. Because of the large-scale …

In-memory computing (IMC) on a monolithic chip for deep learning faces dramatic
challenges on area, yield, and on-chip interconnection cost due to the ever-increasing …

Neural architecture search (NAS) is a promising technique to design efficient and high-
performance deep neural networks (DNNs). As the performance requirements of ML …

In the age of artificial intelligence (AI), the huge data movements between memory and
computing units become the bottleneck of von Neumann architectures, ie, the “memory wall” …

In-memory computing reduces latency and energy consumption of Deep Neural Networks
(DNNs) by reducing the number of off-chip memory accesses. However, crossbar-based in …

With the widespread use of Deep Neural Networks (DNNs), machine learning algorithms
have evolved in two diverse directions—one with ever-increasing connection density for …

Graph convolutional networks (GCNs) have shown remarkable learning capabilities when
processing graph-structured data found inherently in many application areas. GCNs …

RRAM-based in-memory computing (IMC) effectively accelerates deep neural networks
(DNNs) and other machine learning algorithms. On the other hand, in the presence of RRAM …