The cost of data movement in big-data systems motivates careful examination of near-data
processing (NDP) frameworks. The concept of NDP was actively researched in the 1990s …

Solid State Drives (SSDs) were initially developed as faster storage devices intended to
replace conventional magnetic Hard Disk Drives (HDDs). However, high computational …

The ever increasing amount of data being handled in data centers causes an intrinsic
inefficiency: moving data around is expensive in terms of bandwidth, latency, and power …

The von Neumann bottleneck is imposed due to the explosion of data transfers and
emerging data-intensive applications in heterogeneous system architectures. The …

Analytic workloads on terabyte data-sets are often run in the cloud, where application and
storage servers are separate and connected via network. In order to saturate the storage …

Data analysis and retrieval is a widely-used component in existing artificial intelligence
systems. However, each request has to go through each layer across the I/O stack, which …

Today's Hybrid Transactional and Analytical Processing (HTAP) systems, tackle the ever-
growing data in combination with a mixture of transactional and analytical workloads. While …

Recently, there has been a renewed interest of in-storage computing in the context of solid
state drives (SSDs), called" Smart SSDs." Smart SSDs allow application-specific code to …

ABSTRACT Computational Storage Devices (CSDs), which are storage devices including
general-purpose, special-purpose, and/or reconfigurable processing units, are now …

Computer memories continue to serve the role that they first served in the electronic discrete
variable automatic computer (EDVAC) machine documented by John von Neumann, namely …