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20152022
most citedImproving DRAM Performance by Parallelizing Refreshes with Accesses

150 citations · 1.3k across the 73 of their papers we have counts for

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88 papers · 1 filter

cs.AR20222 cited

TuRaN: True Random Number Generation Using Supply Voltage Underscaling in SRAMs

İsmail Emir Yüksel, Ataberk Olgun, Behzad Salami +6

Prior works propose SRAM-based TRNGs that extract entropy from SRAM arrays. SRAM arrays are widely used in a majority of specialized or general-purpose chips that perform the compu…

cs.AR20221 cited

NEON: Enabling Efficient Support for Nonlinear Operations in Resistive RAM-based Neural Network Accelerators

Aditya Manglik, Minesh Patel, Haiyu Mao +4

Resistive Random-Access Memory (RRAM) is well-suited to accelerate neural network (NN) workloads as RRAM-based Processing-in-Memory (PIM) architectures natively support highly-para…

cs.AR20225 cited

HiRA: Hidden Row Activation for Reducing Refresh Latency of Off-the-Shelf DRAM Chips

Abdullah Giray Yağlıkçı, Ataberk Olgun, Minesh Patel +5

DRAM is the building block of modern main memory systems. DRAM cells must be periodically refreshed to prevent data loss. Refresh operations degrade system performance by interferi…

cs.AR20223 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.AR2022

Methodologies, Workloads, and Tools for Processing-in-Memory: Enabling the Adoption of Data-Centric Architectures

Geraldo F. Oliveira, Juan Gómez-Luna, Saugata Ghose +1

The increasing prevalence and growing size of data in modern applications have led to high costs for computation in traditional processor-centric computing systems. Moving large vo…

cs.AR2022

Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases

Geraldo F. Oliveira, Amirali Boroumand, Saugata Ghose +2

Today's computing systems require moving data back-and-forth between computing resources (e.g., CPUs, GPUs, accelerators) and off-chip main memory so that computation can take plac…