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 …

In this work, we identify the potential challenges of ambipolar ferroelectric field effect
transistor (FeFET) in building a single transistor CAM array to perform parallel hamming …

Deep random forest (DRF), which combines deep learning and random forest, exhibits
comparable accuracy, interpretability, low memory and computational overhead to deep …

Novel computer architectures like Compute-in-Memory (CiM) merge the memory and
processing units, mimicking the human brain. Simultaneously, Hyperdimensional …

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 …

Content addressable memory (CAM) has been employed in various data-intensive tasks for
its parallel pattern-matching capability. To enhance the density and efficiency of CAMs …

During the advancement of modern deep learning algorithms, models become increasingly
demanding in computing resources and power-hungry, such that they are considered less …