As von Neumann computing architectures become increasingly constrained by data-
movement overheads, researchers have started exploring in-memory computing (IMC) …

Integrating user ends (UEs), edge servers (ESs), and the cloud into end-edge-cloud
computing (EECC) can enhance the utilization of resources and improve quality of …

Artificial intelligence (AI) processes data-centric applications with minimal effort. However, it
poses new challenges to system design in terms of computational speed and energy …

Area and power-constrained edge devices are increasingly utilized to perform compute
intensive workloads, necessitating increasingly area and power-efficient accelerators. In this …

Processing-using-DRAM (PUD) is a processing-in-memory (PIM) approach that uses a
DRAM array's massive internal parallelism to execute very-wide (eg, 16,384-262,144-bit …

The field of edge computing has witnessed remarkable growth owing to the increasing
demand for real-time processing of data in applications. However, challenges persist due to …

Ongoing climate change calls for fast and accurate weather and climate modeling. However,
when solving large-scale weather prediction simulations, state-of-the-art CPU and GPU …

In-SRAM Computation improves the throughput and energy-efficiency of data-intensive
applications by utilizing parallelism and reducing the data transfers. However, when multiple …

The use of deep learning (DL) to real-world applications, such as computer vision, speech
recognition, and robotics, has become ubiquitous. This can be largely attributed to a virtuous …

Compute memories are memory arrays augmented with dedicated logic to support
arithmetic. They support the efficient execution of data-centric computing patterns, such as …