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

In-memory computing (IMC) refers to non-von Neumann architectures where data are
processed in situ within the memory by taking advantage of physical laws. Among the …

Hyper-Dimensional computing (HDC) is a brain-inspired learning approach for efficient and
robust learning on today's embedded devices. HDC supports single-pass learning, where it …

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

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 …

In a number of machine learning models, an input query is searched across the trained class
vectors to find the closest feature class vector in cosine similarity metric. However …

The finding of the robust ferroelectricity in HfO 2-based thin films is fantastic from the view
point of both the fundamentals and the applications. In this review article, the current …

A fast and efficient search function across the database has been a core component for a
number of data-intensive tasks in machine learning, IoT applications, and inference …

Artificial intelligence has made remarkable advancements in recent years, leading to the
development of algorithms and models capable of handling ever-increasing amounts of …

As a promising alternative to the von Neumann architecture, in-memory computing holds the
promise of delivering a high computing capacity while consuming low power. In this paper …