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

Near-data in-memory processing research has been gaining momentum in recent years.
Typical processing-in-memory architecture places a single or several processing elements …

Near Data Processing, in form of processing in-memory (PIM) and in-storage computing
(ISC) was a very active area of research in computer architectures in the'90s [1, 25]. About 3 …

This book provides a structured introduction of the key concepts and techniques that enable
in-/near-memory computing. For decades, processing-in-memory or near-memory …

The confluence of the recent advances in technology and the ever-growing demand for
large-scale data analytics created a renewed interest in a decades-old concept, processing …

Near-data processing (NDP) refers to augmenting memory or storage with processing
power. Despite its potential for acceleration computing and reducing power requirements …

A major shift from compute-centric to data-centric computing systems can be perceived, as
novel big data workloads like cognitive computing and machine learning strongly enforce …

Computational storage drives (CSD) are solid-state drives (SSD) empowered by general-
purpose processors that can perform in-storage processing. They have the potential to …

This paper explores the benefits and limitations of in-storage processing on current Solid-
State Disk (SSD) architectures. While disk-based in-storage processing has not been widely …

The end of Dennard scaling has made all systemsenergy-constrained. For data-intensive
applications with limitedtemporal locality, the major energy bottleneck is data …