54 citations · 106 across the 8 of their papers we have counts for
8 papers
Enabling Practical Processing in and near Memory for Data-Intensive Computing
Onur Mutlu, Saugata Ghose, Juan Gómez-Luna +1
Modern computing systems suffer from the dichotomy between computation on one side, which is performed only in the processor (and accelerators), and data storage/movement on the ot…
RowHammer: A Retrospective
Onur Mutlu, Jeremie S. Kim
This retrospective paper describes the RowHammer problem in Dynamic Random Access Memory (DRAM), which was initially introduced by Kim et al. at the ISCA 2014 conference~\cite{rowh…
Accelerating Generalized Linear Models with MLWeaving: A One-Size-Fits-All System for Any-precision Learning (Technical Report)
Zeke Wang, Kaan Kara, Hantian Zhang +3
Learning from the data stored in a database is an important function increasingly available in relational engines. Methods using lower precision input data are of special interest…
RowHammer and Beyond
Onur Mutlu
We will discuss the RowHammer problem in DRAM, which is a prime (and likely the first) example of how a circuit-level failure mechanism in Dynamic Random Access Memory (DRAM) can c…
An Analytical Model for Performance and Lifetime Estimation of Hybrid DRAM-NVM Main Memories
Reza Salkhordeh, Onur Mutlu, Hossein Asadi
NVMs have promising advantages (e.g., lower idle power, higher density) over the existing predominant main memory technology, DRAM. Yet, NVMs also have disadvantages (e.g., limited…
Processing Data Where It Makes Sense: Enabling In-Memory Computation
Onur Mutlu, Saugata Ghose, Juan Gómez-Luna +1
Today's systems are overwhelmingly designed to move data to computation. This design choice goes directly against at least three key trends in systems that cause performance, scala…