Compute-in-memory (CIM) is a new computing paradigm that addresses the memory-wall
problem in hardware accelerator design for deep learning. The input vector and weight …

A continuous exponential rise has been observed in the storage and processing of the data
that may not curtail in the foreseeable future. The required data processing speed and …

The discovery of ferroelectricity in oxides that are compatible with modern semiconductor
manufacturing processes, such as hafnium oxide, has led to a re-emergence of the …

Artificial intelligence edge devices should offer high inference accuracy and rapid response
times, as well as being energy efficient. Ensuring the security of these devices against …

Non-volatile computing-in-memory (nvCIM) architecture can reduce the latency and energy
consumption of artificial intelligence computation by minimizing the movement of data …

The ability to scale two-dimensional (2D) material thickness down to a single monolayer
presents a promising opportunity to realize high-speed energy-efficient memristors. Here …

This paper presents an overview of emerging memory technologies. It begins with the
presentation of stand-alone and embedded memory technology evolution, since the …

To efficiently deploy machine learning applications to the edge, compute-in-memory (CIM)
based hardware accelerator is a promising solution with improved throughput and energy …

From our own experience, we know that there is a gap to bridge between the scientists
focused on basic material research and their counterparts in a close-to-application …

Silicon channel ferroelectric field-effect transistors (FeFETs) with low-k interfacial layer (IL)
between ferroelectric and silicon channel suffers from high write voltage, limited write …