Computing in memory (CIM) could be used to overcome the von Neumann bottleneck and to
provide sustainable improvements in computing throughput and energy efficiency …

For state-of-the-art artificial intelligence (AI) accelerators, there have been large advances in
both all-digital and analog/mixed-signal circuit-based designs. This article presents a …

In this article, we review the development of emerging memory-based neuromorphic
computing. First, we discuss the motivation and advantages of this approach. We then …

In-memory computing (IMC) architectures have emerged as a compelling platform to
implement energy-efficient machine learning (ML) systems. However, today, the energy …

In-memory-computing is emerging as an efficient hardware paradigm for deep neural
network accelerators at the edge, enabling to break the memory wall and exploit massive …

This review explores the intersection of bio-plausible artificial intelligence in the form of
spiking neural networks (SNNs) with the analog in-memory computing (IMC) domain …

In-Memory Computing (IMC) has emerged as a promising paradigm for energy-efficient,
throughput-efficient and area-efficient machine learning at the edge. However, the …

SRAM-based analog in-memory computing (IMC) architectures have demonstrated high
energy efficiency and compute density over digital accelerators for machine learning …

Analog in-memory computing architectures (IMCs) have exhibited high energy efficiency
over conventional digital architectures. The use of resistive memory arrays such as magnetic …

Analog computing-in-memory (CIM) has been widely explored for computing neural
networks (NNs) efficiently. However, most analog CIM implementations trade compute …