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 …

Several manufacturers have already started to commercialize near-bank Processing-In-
Memory (PIM) architectures, after decades of research efforts. Near-bank PIM architectures …

The amalgamation of artificial intelligence with Internet of Things (AIoT) devices have seen a
rapid surge in growth, largely due to the effective implementation of deep neural network …

Responding to the" datacenter tax" and" killer microseconds" problems for memory-intensive
datacenter applications, diverse solutions including Smart NIC-based ones have been …

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 …

We present Decoupled Vector Runahead (DVR), an in-core prefetching technique,
executing separately to the main application thread, that exploits massive amounts of …

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

Various near-data processing (NDP) designs have been proposed to alleviate the memory
wall challenge for data-intensive applications. Among them, near-DRAM-bank NDP …

Applications with low data reuse and frequent irregular memory accesses, such as graph or
sparse linear algebra workloads, fail to scale well due to memory bottlenecks and poor core …

In-memory computing with large last-level caches is promising to dramatically alleviate data
movement bottlenecks and expose massive bitline-level parallelization opportunities …