60 citations · 222 across the 56 of their papers we have counts for
12 papers · 1 filter
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…
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…
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…
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…
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…
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…
Revisiting Main Memory-Based Covert and Side Channel Attacks in the Context of Processing-in-Memory
F. Nisa Bostanci, Konstantinos Kanellopoulos, Ataberk Olgun +6
We introduce IMPACT, a set of high-throughput main memory-based timing attacks that leverage characteristics of processing-in-memory (PiM) architectures to establish covert and sid…
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…
Virtuoso: Enabling Fast and Accurate Virtual Memory Research via an Imitation-based Operating System Simulation Methodology
Konstantinos Kanellopoulos, Konstantinos Sgouras, F. Nisa Bostanci +7
The unprecedented growth in data demand from emerging applications has turned virtual memory (VM) into a major performance bottleneck. Researchers explore new hardware/OS co-design…
CoMeT: Count-Min-Sketch-based Row Tracking to Mitigate RowHammer at Low Cost
F. Nisa Bostanci, Ismail Emir Yuksel, Ataberk Olgun +5
We propose a new RowHammer mitigation mechanism, CoMeT, that prevents RowHammer bitflips with low area, performance, and energy costs in DRAM-based systems at very low RowHammer th…
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…
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…