Ferroelectric field effect transistors (FeFETs) have attracted attention as next-generation
devices as they can serve as a synaptic device for neuromorphic implementation and a one …

Ferroelectric transistors (FeFETs) based on doped hafnium oxide (HfO2) have received
much attention due to their technological potential in terms of scalability, high-speed, and …

Deep neural networks are efficient at learning from large sets of labelled data, but struggle to
adapt to previously unseen data. In pursuit of generalized artificial intelligence, one …

Convolutional neural networks (CNNs) have gained much attention because they can
provide superior complex image recognition through convolution operations. Convolution …

Memory isolation is critical for system reliability, security, and safety. Unfortunately, read
disturbance can break memory isolation in modern DRAM chips. For example, RowHammer …

Recent advances in oxide ferroelectric (FE) materials have rejuvenated the field of low-
power, nonvolatile memories and made FE memories a commercial reality. Despite these …

Ferroelectric field effect transistors (FeFETs) are being actively investigated with the
potential for in-memory computing (IMC) over other nonvolatile memories (NVMs). Content …

Hyperdimensional computing (HDC) is a brain-inspired computational framework that relies
on long hypervectors (HVs) for learning. In HDC, computational operations consist of simple …

We provide an overview of recent developments and future directions in the RowHammer
vulnerability that plagues modern DRAM (Dynamic Random Memory Access) chips, which …

The deluge of sensors and data generating devices has driven a paradigm shift in modern
computing from arithmetic-logic centric to data-centric processing. Data-centric processing …