Ferroelectric materials, the charge equivalent of magnets, have been the subject of
continued research interest since their discovery more than 100 years ago. The …

Ferroelectric materials are considered ideal for emerging memory devices owing to their
characteristic remanent polarization, which can be switched by applying a sufficient electric …

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 …

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

With the discovery of ferroelectricity in HfO 2 based thin films and the co-integration of
ferroelectric field effect transistors (FeFET) into standard high-k metal gate (HKMG) CMOS …

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

The dramatic rise of data‐intensive workloads has revived application‐specific
computational hardware for continuing speed and power improvements, frequently achieved …

Nonvolatile logic devices have attracted intensive research attentions recently for energy
efficiency computing, where data computing and storage can be realized in the same device …

Pattern searches, a key operation in many data analytic applications, often deal with data
represented by multiple states per dimension. However, hash tables, a common software …

Brain-inspired hyperdimensional (HD) computing emulates cognitive tasks by computing
with long binary vectors-also know as hypervectors-as opposed to computing with numbers …