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

Transformers have revolutionized various real-world applications from natural language
processing to computer vision. However, traditional von-Neumann computing paradigm …

Today, there are a plethora of In-Memory Computing (IMC) devices-SRAMs, PCMs &
FeFETs, that emulate convolutions on crossbar-arrays with high throughput. Each IMC …

Extracting behavioral models of RRAM devices is challenging due to their unique “memory”
behaviors and rapid developments, for which well-established modeling frameworks and …

In the application of sustainable Nature-based Solution (NbS) for coastal engineering, a
significant challenge lies in determining the effectiveness of these NbS approaches in …

Resistive random access memory (RRAM) emerges as a promising technology for
develo** energy-efficient deep neural network (DNN) accelerators, owing to its analog …

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

Abstract In-memory computing (IMC)-based hardware reduces latency and energy
consumption for compute-intensive machine learning (ML) applications. Several …

As society advances through the big data revolution and technological innovation, sectors
such as automotive, healthcare, and entertainment are becoming increasingly real-time and …

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 …