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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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23 papers · 1 filter

cs.AR2025

DCC: Data-Centric Compilation of Machine Learning Kernels for Processing-In-Memory Architectures

Peiming Yang, Sankeerth Durvasula, Ivan Fernandez +4

High-performance Host processors can integrate Processing-In-Memory (PIM) devices, which can accelerate memory-intensive kernels of Machine Learning (ML) models, including Large La…

cs.AR2025

In-DRAM True Random Number Generation Using Simultaneous Multiple-Row Activation: An Experimental Study of Real DRAM Chips

Ismail Emir Yuksel, Ataberk Olgun, F. Nisa Bostanci +5

In this work, we experimentally demonstrate that it is possible to generate true random numbers at high throughput and low latency in commercial off-the-shelf (COTS) DRAM chips by…

cs.AR2025

ColumnDisturb: Understanding Column-based Read Disturbance in Real DRAM Chips and Implications for Future Systems

İsmail Emir Yüksel, Ataberk Olgun, F. Nisa Bostancı +3

We experimentally demonstrate a new widespread read disturbance phenomenon, ColumnDisturb, in real commodity DRAM chips. By repeatedly opening or keeping a DRAM row (aggressor row)…

cs.AR2025

Cleaning up the Mess: Re-Evaluating the Real-System Modeling Accuracy of Ramulator 2.0

F. Nisa Bostanci, Haocong Luo, Ataberk Olgun +4

A MICRO 2024 best paper runner-up publication (the Mess paper) with all three artifact badges awarded (including ``Reproducible'') proposes a new benchmark to evaluate real and sim…

cs.DC2025

TeraAgent: A Distributed Agent-Based Simulation Engine for Simulating Half a Trillion Agents

Lukas Breitwieser, Ahmad Hesam, Abdullah Giray Yağlıkçı +3

Agent-based simulation is a method for studying highly complex systems in disciplines as diverse as biology, medicine, sociology, economics, finance, and more. In this method, an a…

cs.AR2025★ 8 cited

MARS: Processing-In-Memory Acceleration of Raw Signal Genome Analysis Inside the Storage Subsystem

Melina Soysal, Konstantina Koliogeorgi, Can Firtina +8

Raw signal genome analysis (RSGA) has emerged as a promising approach to enable real-time genome analysis by directly analyzing raw electrical signals. However, rapid advancements…

cs.AR2025

EasyDRAM: An FPGA-based Infrastructure for Fast and Accurate End-to-End Evaluation of Emerging DRAM Techniques

Oğuzhan Canpolat, Ataberk Olgun, David Novo +2

DRAM is a critical component of modern computing systems. Recent works propose numerous techniques (that we call DRAM techniques) to enhance DRAM-based computing systems' throughpu…

cs.AR2025

PuDHammer: Experimental Analysis of Read Disturbance Effects of Processing-using-DRAM in Real DRAM Chips

Ismail Emir Yuksel, Akash Sood, Ataberk Olgun +6

Processing-using-DRAM (PuD) is a promising paradigm for alleviating the data movement bottleneck using DRAM's massive internal parallelism and bandwidth to execute very wide operat…

cs.CL2025

REIS: A High-Performance and Energy-Efficient Retrieval System with In-Storage Processing

Kangqi Chen, Andreas Kosmas Kakolyris, Rakesh Nadig +7

Large Language Models (LLMs) face an inherent challenge: their knowledge is confined to the data that they have been trained on. To overcome this issue, Retrieval-Augmented Generat…

q-bio.GN2025★ 2 cited

Processing-in-memory for genomics workloads

William Andrew Simon, Leonid Yavits, Konstantina Koliogeorgi +12

Low-cost, high-throughput DNA and RNA sequencing (HTS) data is the backbone of the life sciences. Genome sequencing is now becoming a part of Predictive, Preventive, Personalized,…

cs.AR2025★ 2 cited

Accelerating Triangle Counting with Real Processing-in-Memory Systems

Lorenzo Asquini, Manos Frouzakis, Juan Gómez-Luna +3

Triangle Counting (TC) is a procedure that involves enumerating the number of triangles within a graph. It has important applications in numerous fields, such as social or biologic…

cs.AR2025

Memory-Centric Computing: Solving Computing's Memory Problem

Onur Mutlu, Ataberk Olgun, Ismail Emir Yuksel

Computing has a huge memory problem. The memory system, consisting of multiple technologies at different levels, is responsible for most of the energy consumption, performance bott…

cs.AR2025

PIMDAL: Mitigating the Memory Bottleneck in Data Analytics using a Real Processing-in-Memory System

Manos Frouzakis, Juan Gómez-Luna, Geraldo F. Oliveira +2

Database Management Systems (DBMSs) are crucial for efficient data management and analytics, and are used in several different application domains. Due to the increasing volume of…

cs.AR2025

SAGe: A Lightweight Algorithm-Architecture Co-Design for Mitigating the Data Preparation Bottleneck in Large-Scale Genome Sequence Analysis

Nika Mansouri Ghiasi, Talu Güloglu, Harun Mustafa +8

Genome sequence analysis, which examines the DNA sequences of organisms, drives advances in many critical medical and biotechnological fields. Given its importance and the exponent…

cs.CR2025

CIPHERMATCH: Accelerating Homomorphic Encryption-Based String Matching via Memory-Efficient Data Packing and In-Flash Processing

Mayank Kabra, Rakesh Nadig, Harshita Gupta +7

Homomorphic encryption (HE) allows secure computation on encrypted data without revealing the original data, providing significant benefits for privacy-sensitive applications. Many…

cs.AR2025

Harmonia: Enhancing Data Placement and Migration in Hybrid Storage Systems via Multi-Agent Reinforcement Learning

Rakesh Nadig, Vamanan Arulchelvan, Rahul Bera +7

Modern high-performance computing (HPC) environments rely on hybrid storage systems (HSS) that combine multiple storage devices with diverse latency, bandwidth, endurance, and capa…

cs.CR2025

Understanding and Mitigating Covert Channel and Side Channel Vulnerabilities Introduced by RowHammer Defenses

F. Nisa Bostancı, Oğuzhan Canpolat, Ataberk Olgun +5

DRAM chips are vulnerable to read disturbance phenomena (e.g., RowHammer and RowPress), where repeatedly accessing or keeping open a DRAM row causes bitflips in nearby rows. Attack…

cs.AR2025

PAPI: Exploiting Dynamic Parallelism in Large Language Model Decoding with a Processing-In-Memory-Enabled Computing System

Yintao He, Haiyu Mao, Christina Giannoula +6

Large language models (LLMs) are widely used for natural language understanding and text generation. An LLM model relies on a time-consuming step called LLM decoding to generate ou…

cs.AR2025

Variable Read Disturbance: An Experimental Analysis of Temporal Variation in DRAM Read Disturbance

Ataberk Olgun, F. Nisa Bostanci, Ismail Emir Yuksel +6

Modern DRAM chips are subject to read disturbance errors. State-of-the-art read disturbance mitigations rely on accurate and exhaustive characterization of the read disturbance thr…

cs.OS2025

Ariadne: A Hotness-Aware and Size-Adaptive Compressed Swap Technique for Fast Application Relaunch and Reduced CPU Usage on Mobile Devices

Yu Liang, Aofeng Shen, Chun Jason Xue +9

Growing application memory demands and concurrent usage are making mobile device memory scarce. When memory pressure is high, current mobile systems use a RAM-based compressed swap…

cs.AR2025

Understanding RowHammer Under Reduced Refresh Latency: Experimental Analysis of Real DRAM Chips and Implications on Future Solutions

Yahya Can Tuğrul, A. Giray Yağlıkçı, İsmail Emir Yüksel +6

RowHammer is a major read disturbance mechanism in DRAM where repeatedly accessing (hammering) a row of DRAM cells (DRAM row) induces bitflips in physically nearby DRAM rows (victi…

cs.CR2025

Chronus: Understanding and Securing the Cutting-Edge Industry Solutions to DRAM Read Disturbance

Oğuzhan Canpolat, A. Giray Yağlıkçı, Geraldo F. Oliveira +6

We 1) present the first rigorous security, performance, energy, and cost analyses of the state-of-the-art on-DRAM-die read disturbance mitigation method, Per Row Activation Countin…

cs.AR2025

Proteus: Enabling High-Performance Processing-Using-DRAM with Dynamic Bit-Precision, Adaptive Data Representation, and Flexible Arithmetic

Geraldo F. Oliveira, Mayank Kabra, Yuxin Guo +8

Processing-using-DRAM (PUD) is a paradigm where the analog operational properties of DRAM are used to perform bulk logic operations. While PUD promises high throughput at low energ…