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

We propose a novel resistive edit distance-tolerant content addressable memory for
computational genomics applications, particularly for detection and identification of …

Machine learning and data analytics applications increasingly suffer from the high latency
and energy consumption of conventional von Neumann architectures. Recently, several in …

Associative access is widely used in fundamental microarchitectural components, such as
caches and TLBs. However, associative (or content addressable) memories (CAMs) have …

In-memory computing (IMC) architecture emerges as a promising paradigm, improving the
energy efficiency of multiply-and-accumulate (MAC) operations within deep neural networks …

The exponential growth of data across various domains of human society necessitates the
rapid and efficient data processing. In many contemporary data-intensive applications …

Rapid advancements in artificial intelligence have given rise to transformative models,
profoundly impacting our lives. These models demand massive volumes of data to operate …

A novel one-transistor (1T) ternary content-addressable memory (TCAM) in which the don't
care state 'X'is implemented based on the localized ferroelectric polarization switching in the …

Compute-in-memory (CiM) is a promising solution for addressing the challenges of artificial
intelligence (AI) and the Internet of Things (IoT) hardware such as “memory wall” issue …

Ternary content addressable memory (TCAM), widely used in network routers and high-
associativity caches, is gaining popularity in machine learning and data-analytic …