124 citations · 433 across the 23 of their papers we have counts for
8 papers · 1 filter
ALP: Alleviating CPU-Memory Data Movement Overheads in Memory-Centric Systems
Nika Mansouri Ghiasi, Nandita Vijaykumar, Geraldo F. Oliveira +7
Partitioning applications between NDP and host CPU cores causes inter-segment data movement overhead, which is caused by moving data generated from one segment (e.g., instructions,…
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…
DRAM Bender: An Extensible and Versatile FPGA-based Infrastructure to Easily Test State-of-the-art DRAM Chips
Ataberk Olgun, Hasan Hassan, A. Giray Yağlıkçı +6
To understand and improve DRAM performance, reliability, security and energy efficiency, prior works study characteristics of commodity DRAM chips. Unfortunately, state-of-the-art…
SpyHammer: Understanding and Exploiting RowHammer under Fine-Grained Temperature Variations
Lois Orosa, Ulrich Rührmair, A. Giray Yaglikci +7
RowHammer is a DRAM vulnerability that can cause bit errors in a victim DRAM row solely by accessing its neighboring DRAM rows at a high-enough rate. Recent studies demonstrate tha…
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…
Understanding RowHammer Under Reduced Wordline Voltage: An Experimental Study Using Real DRAM Devices
A. Giray Yağlıkçı, Haocong Luo, Geraldo F. de Oliviera +7
RowHammer is a circuit-level DRAM vulnerability, where repeatedly activating and precharging a DRAM row, and thus alternating the voltage of a row's wordline between low and high v…
EcoFlow: Efficient Convolutional Dataflows for Low-Power Neural Network Accelerators
Lois Orosa, Skanda Koppula, Yaman Umuroglu +5
Dilated and transposed convolutions are widely used in modern convolutional neural networks (CNNs). These kernels are used extensively during CNN training and inference of applicat…
DR-STRaNGe: End-to-End System Design for DRAM-based True Random Number Generators
F. Nisa Bostancı, Ataberk Olgun, Lois Orosa +5
Random number generation is an important task in a wide variety of critical applications including cryptographic algorithms, scientific simulations, and industrial testing tools. T…