The growing adoption of data-driven applications, such as artificial intelligence (AI), is
transforming the way we interact with technology. Currently, the deployment of AI and …

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

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

Transformer models represent the cutting edge of Deep Neural Networks (DNNs) and excel
in a wide range of machine learning tasks. However, processing these models demands …

The emergence of advanced artificial intelligence (AI) models has driven the development of
frameworks and approaches that focus on automating model training and hyperparameter …

Multiple research vectors represent possible paths to improved energy and performance
metrics at the application-level. There are active efforts with respect to emerging logic …

Content addressable memory (CAM) stands out as an efficient hardware solution for
memory-intensive search operations by supporting parallel computation in memory …

In-memory computing-based systems deliver en-hanced performance by eliminating data
movement between computational and storage units. Among various in-memory computing …

Due to the high cost of data movement in the traditional von Neumann architecture,
particularly in many data-intensive workloads, in-memory computing (IMC), by integrating …