activity
20192022
most citedBlockHammer: Preventing RowHammer at Low Cost by Blacklisting Rapidly-Accessed DRAM Rows

124 citations · 210 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AR2022★ 3 cited

Flash-Cosmos: In-Flash Bulk Bitwise Operations Using Inherent Computation Capability of NAND Flash Memory

Jisung Park, Roknoddin Azizi, Geraldo F. Oliveira +6

Bulk bitwise operations, i.e., bitwise operations on large bit vectors, are prevalent in a wide range of important application domains, including databases, graph processing, genom…

cs.CR2021★ 124 cited

BlockHammer: Preventing RowHammer at Low Cost by Blacklisting Rapidly-Accessed DRAM Rows

Abdullah Giray Yağlıkçı, Minesh Patel, Jeremie S. Kim +9

Aggressive memory density scaling causes modern DRAM devices to suffer from RowHammer, a phenomenon where rapidly activating a DRAM row can cause bit-flips in physically-nearby row…

cs.AR2020★ 1 cited

Revisiting RowHammer: An Experimental Analysis of Modern DRAM Devices and Mitigation Techniques

Jeremie S. Kim, Minesh Patel, A. Giray Yaglikci +4

In order to shed more light on how RowHammer affects modern and future devices at the circuit-level, we first present an experimental characterization of RowHammer on 1580 DRAM chi…

cs.DC2019★ 81 cited

SMASH: Co-designing Software Compression and Hardware-Accelerated Indexing for Efficient Sparse Matrix Operations

Konstantinos Kanellopoulos, Nandita Vijaykumar, Christina Giannoula +6

Important workloads, such as machine learning and graph analytics applications, heavily involve sparse linear algebra operations. These operations use sparse matrix compression as…

cs.DC2019★ 1 cited

EDEN: Enabling Energy-Efficient, High-Performance Deep Neural Network Inference Using Approximate DRAM

Skanda Koppula, Lois Orosa, Abdullah Giray Yağlıkçı +4

The effectiveness of deep neural networks (DNN) in vision, speech, and language processing has prompted a tremendous demand for energy-efficient high-performance DNN inference syst…