Processing-In-Memory (PIM) accelerators have the potential to efficiently run Deep Neural
Network (DNN) inference by reducing costly data movement and by using resistive RAM …

Non-volatile memory (NVM) such as RRAM and PCM has become the key component in
high energy efficiency computing-in-memory (CIM) architectures. However, the computing …

Recently memristors have emerged as a form of nonvolatile memory that is based on the
principle of ion transport in solid electrolytes under the impact of an external electric field. It …

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 …

Embedded memory, which heavily relies on the manufacturing process, has been widely
adopted in various industrial applications. As the field of embedded memory continues to …

After the success of the transformer networks on natural language processing (NLP), the
application of transformers to computer vision (CV) has followed suit to deliver …

Compute-In-Memory (CiM) is a promising solution to accelerate Deep Neural Networks
(DNNs) as it can avoid energy-intensive DNN weight movement and use memory arrays to …

Over the past decade, numerous compute-in-memory (CIM) platforms have been proposed
in the literature. While emerging non-volatile memory based analog CIM (ACIM) has been …

Compute-in-memory (CIM) hardware accelerator has been emerged as a promising
paradigm for executing the artificial intelligence (AI) tasks owing to its superior energy …

Artificial intelligence (AI) has experienced substantial advancements recently, notably with
the advent of large-scale language models (LLMs) employing mixture-of-experts (MoE) …