The conventional approach of moving data to the CPU for computation has become a
significant performance bottleneck for emerging scale-out data-intensive applications due to …

The conventional approach of moving stored data to the CPU for computation has become a
major performance bottleneck for emerging scale-out data-intensive applications due to their …

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 …

The performance gap between memory and CPU has grown exponentially. To bridge this
gap, hardware architects have proposed near-memory computing (also called processing-in …

The cost of moving data between the memory/storage units and the compute units is a major
contributor to the execution time and energy consumption of modern workloads in …

Many modern and emerging applications must process increasingly large volumes of data.
Unfortunately, prevalent computing paradigms are not designed to efficiently handle such …

Processing-in-memory (PIM) is rapidly rising as a viable solution for the memory wall crisis,
rebounding from its unsuccessful attempts in 1990s due to practicality concerns, which are …

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

The increasing demand for extracting value out of ever-growing data poses an ongoing
challenge to system designers, a task only made trickier by the end of Dennard scaling. As …

The physical memory capacity of servers is expected to increase drastically with deployment
of the forthcoming non-volatile memory technologies. This is a welcomed improvement for …