Facing the computing demands of Internet of things (IoT) and artificial intelligence (AI), the
cost induced by moving the data between the central processing unit (CPU) and memory is …

Abstract Three‐dimensional (3D) image reconstruction of tumours can visualise their
structures with precision and high resolution. In this article, GAN‐LSTM‐3D method is …

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

Training machine learning (ML) algorithms is a computationally intensive process, which is
frequently memory-bound due to repeatedly accessing large training datasets. As a result …

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” …

Data movement between memory and processors is a major bottleneck in modern
computing systems. The processing-in-memory (PIM) paradigm aims to alleviate this …

Training machine learning (ML) algorithms is a computationally intensive process, which is
frequently memory-bound due to repeatedly accessing large training datasets. As a result …

Graph convolutional networks (GCNs) have been applied successfully in social networks
and recommendation systems to analyze graph data. Unlike conventional neural networks …

In Analog Computing-in-Memory (CIM) neural network accelerators, analog-to-digital
converters (ADCs) are required to convert the analog partial sums generated from a CIM …

Reinforcement Learning (RL) trains agents to learn optimal behavior by maximizing reward
signals from experience datasets. However, RL training often faces memory limitations …