most citedSwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

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.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.LG20241 cited

SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory Systems

Kailash Gogineni, Sai Santosh Dayapule, Juan Gómez-Luna +6

Reinforcement Learning (RL) trains agents to learn optimal behavior by maximizing reward signals from experience datasets. However, RL training often faces memory limitations, lead…