Spiking neural networks (SNNs) are an active research domain toward energy-efficient
machine intelligence. Compared to conventional artificial neural networks (ANNs), SNNs …

Memristors provide a tempting solution for weighted synapse connections in neuromorphic
computing due to their size and non-volatile nature. However, memristors are unreliable in …

Compute-in-Memory (CIM) implemented with Resistive-Random-Access-Memory (RRAM)
crossbars is a promising approach for accelerating Convolutional Neural Network (CNN) …

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 …

Compute-in-Memory (CIM) implemented with Resistive-Random-Access-Memory (RRAM)
crossbars is a promising approach for Deep Neural Network (DNN) acceleration. As the …

Brain-inspired hyperdimensional computing (HDC) is continuously gaining remarkable
attention. It is a promising alternative to traditional machine-learning approaches due to its …

RRAM-based in-memory computing (IMC) effectively accelerates deep neural networks
(DNNs). Furthermore, model compression techniques, such as quantization and pruning …

A memristor array has emerged as a potential computing hardware for artificial intelligence
(AI). It has an inherent memory effect that allows information storage in the form of easily …

Artificial intelligence (AI) systems have made significant impact on the society in the recent
years in a wide range of fields, including healthcare, transportation, and finances. In …

Abstract In-memory computing (IMC)-based hardware reduces latency as well as energy
consumption for compute-intensive machine learning (ML) applications. Till date, several …