Juan Gómez-Luna

TL;DR: In this paper, the authors discuss some recent research that aims to practically enable computation close to data and discuss at least two promising directions for processing-in-memory (PIM): (1) performing massively-parallel bulk operations in memory by exploiting the analog operational properties of DRAM, with low-cost changes, and (2) exploiting the logic layer in 3D-stacked memory technology to accelerate important data-intensive applications.

TL;DR: SIMDRAM as mentioned in this paper is a general-purpose processing-using-DRAM framework that enables the efficient implementation of complex operations and provides a flexible mechanism to support the implementation of arbitrary user-defined operations.

TL;DR: This chapter discusses recent research that aims to practically enable computation close to data, an approach called processing-in-memory (PIM).

TL;DR: GenASM as discussed by the authors accelerates read alignment for both long reads and short reads, with 3.7× the performance of a state-of-the-art pre-alignment filter.

TL;DR: This article describes the work on systematically identifying opportunities for PIM in real applications and quantifies potential gains for popular emerging applications (e.g., machine learning, data analytics, genome analysis) and describes challenges that remain for the widespread adoption of PIM.