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20222026
most citedSpyHammer: Understanding and Exploiting RowHammer under Fine-Grained Temperature Variations

13 citations · 57 across the 75 of their papers we have counts for

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Showing 2024 · cs.ARShow all

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cs.AR2024

Memory-Centric Computing: Recent Advances in Processing-in-DRAM

Onur Mutlu, Ataberk Olgun, Geraldo F. Oliveira +1

Memory-centric computing aims to enable computation capability in and near all places where data is generated and stored. As such, it can greatly reduce the large negative performa…

cs.AR2024★ 1 cited

MegIS: High-Performance, Energy-Efficient, and Low-Cost Metagenomic Analysis with In-Storage Processing

Nika Mansouri Ghiasi, Mohammad Sadrosadati, Harun Mustafa +9

Metagenomics has led to significant advances in many fields. Metagenomic analysis commonly involves the key tasks of determining the species present in a sample and their relative…

cs.AR2024

Constable: Improving Performance and Power Efficiency by Safely Eliminating Load Instruction Execution

Rahul Bera, Adithya Ranganathan, Joydeep Rakshit +8

Load instructions often limit instruction-level parallelism (ILP) in modern processors due to data and resource dependences they cause. Prior techniques like Load Value Prediction…

cs.AR2024★ 1 cited

An Experimental Characterization of Combined RowHammer and RowPress Read Disturbance in Modern DRAM Chips

Haocong Luo, Ismail Emir Yüksel, Ataberk Olgun +3

DRAM read disturbance can break memory isolation, a fundamental property to ensure system robustness (i.e., reliability, security, safety). RowHammer and RowPress are two different…

cs.AR2024

RowPress Vulnerability in Modern DRAM Chips

Haocong Luo, Ataberk Olgun, A. Giray Yağlıkçı +6

Memory isolation is a critical property for system reliability, security, and safety. We demonstrate RowPress, a DRAM read disturbance phenomenon different from the well-known RowH…

cs.AR2024

Simultaneous Many-Row Activation in Off-the-Shelf DRAM Chips: Experimental Characterization and Analysis

Ismail Emir Yuksel, Yahya Can Tugrul, F. Nisa Bostanci +8

We experimentally analyze the computational capability of commercial off-the-shelf (COTS) DRAM chips and the robustness of these capabilities under various timing delays between DR…

cs.AR2024★ 1 cited

AERO: Adaptive Erase Operation for Improving Lifetime and Performance of Modern NAND Flash-Based SSDs

Sungjun Cho, Beomjun Kim, Hyunuk Cho +4

This work investigates a new erase scheme in NAND flash memory to improve the lifetime and performance of modern solid-state drives (SSDs). In NAND flash memory, an erase operation…

cs.AR2024★ 1 cited

PIM-Opt: Demystifying Distributed Optimization Algorithms on a Real-World Processing-In-Memory System

Steve Rhyner, Haocong Luo, Juan Gómez-Luna +6

Modern Machine Learning (ML) training on large-scale datasets is a very time-consuming workload. It relies on the optimization algorithm Stochastic Gradient Descent (SGD) due to it…

cs.AR2024

PUMA: Efficient and Low-Cost Memory Allocation and Alignment Support for Processing-Using-Memory Architectures

Geraldo F. Oliveira, Emanuele G. Esposito, Juan Gómez-Luna +1

Processing-using-DRAM (PUD) architectures impose a restrictive data layout and alignment for their operands, where source and destination operands (i) must reside in the same DRAM…

cs.AR2024★ 1 cited

MIMDRAM: An End-to-End Processing-Using-DRAM System for High-Throughput, Energy-Efficient and Programmer-Transparent Multiple-Instruction Multiple-Data Processing

Geraldo F. Oliveira, Ataberk Olgun, Abdullah Giray Yağlıkçı +4

Processing-using-DRAM (PUD) is a processing-in-memory (PIM) approach that uses a DRAM array's massive internal parallelism to execute very-wide data-parallel operations, in a singl…

cs.AR2024★ 1 cited

Functionally-Complete Boolean Logic in Real DRAM Chips: Experimental Characterization and Analysis

Ismail Emir Yuksel, Yahya Can Tugrul, Ataberk Olgun +7

Processing-using-DRAM (PuD) is an emerging paradigm that leverages the analog operational properties of DRAM circuitry to enable massively parallel in-DRAM computation. PuD has the…

cs.AR2024★ 3 cited

PyGim: An Efficient Graph Neural Network Library for Real Processing-In-Memory Architectures

Christina Giannoula, Peiming Yang, Ivan Fernandez +7

Graph Neural Networks (GNNs) are emerging ML models to analyze graph-structure data. Graph Neural Network (GNN) execution involves both compute-intensive and memory-intensive kerne…

cs.AR2024

Rethinking the Producer-Consumer Relationship in Modern DRAM-Based Systems

Minesh Patel, Taha Shahroodi, Aditya Manglik +4

Generational improvements to commodity DRAM throughout half a century have long solidified its prevalence as main memory across the computing industry. However, overcoming today's…