Package-level integration using multi-chip-modules (MCMs) is a promising approach for
building large-scale systems. Compared to a large monolithic die, an MCM combines many …

Data movement between the CPU and main memory is a first-order obstacle against improv
ing performance, scalability, and energy efficiency in modern systems. Computer systems …

Specialized on-chip accelerators are widely used to improve the energy efficiency of
computing systems. Recent advances in memory technology have enabled near-data …

Memory-centric computing aims to enable computation capability in and near all places
where data is generated and stored. As such, it can greatly reduce the large negative …

Near-Data-Processing (NDP) architectures present a promising way to alleviate data
movement costs and can provide significant performance and energy benefits to parallel …

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 …

Due to the amount of data involved in emerging deep learning and big data applications,
operations related to data movement have quickly become a bottleneck. Data-centric …

Heterogeneous graph neural networks (HGNNs) based on metapath exhibit powerful
capturing of rich structural and semantic information in the heterogeneous graph. HGNNs …

Data movement between the main memory and the processor is a key contributor to
execution time and energy consumption in memory-intensive applications. This data …

Stencil computations are commonly used in a wide variety of scientific applications, ranging
from large-scale weather prediction to solving partial differential equations. Stencil …