HARP: Practically and Effectively Identifying Uncorrectable Errors in Memory Chips That Use On-Die Error-Correcting Codes


Abstract: "State-of-the-art techniques for addressing scaling-related main memory errors identify and repair bits that are at risk of error from within the memory controller. Unfortunately, modern main memory chips internally use on-die error correcting codes (on-die ECC) that obfuscate the memory controller's view of errors, complicating the process of identifying at-risk bits (i.e., error pr... » read more

Uncovering In-DRAM RowHammer Protection Mechanisms: A New Methodology, Custom RowHammer Patterns, and Implications


Abstract: "The RowHammer vulnerability in DRAM is a critical threat to system security. To protect against RowHammer, vendors commit to security-through-obscurity: modern DRAM chips rely on undocumented, proprietary, on-die mitigations, commonly known as Target Row Refresh (TRR). At a high level, TRR detects and refreshes potential RowHammer-victim rows, but its exact are not openly disclose... » read more

Improving DRAM Performance, Security, and Reliability by Understanding and Exploiting DRAM Timing Parameter Margins


Abstract: "Characterization of real DRAM devices has enabled findings in DRAM device properties, which has led to proposals that significantly improve overall system performance by reducing DRAM access latency and power consumption. In addition to improving system performance, a deeper understanding of DRAM technology via characterization can also improve device reliability and security. The... » read more

A Deeper Look into RowHammer’s Sensitivities: Experimental Analysis of Real DRAM Chips and Implications on Future Attacks and Defenses


Abstract "RowHammer is a circuit-level DRAM vulnerability where repeatedly accessing (i.e., hammering) a DRAM row can cause bit flips in physically nearby rows. The RowHammer vulnerability worsens as DRAM cell size and cell-to-cell spacing shrink. Recent studies demonstrate that modern DRAM chips, including chips previously marketed as RowHammer-safe, are even more vulnerable to RowHammer than... » read more

Accelerating Inference of Convolutional Neural Networks Using In-memory Computing


Abstract: "In-memory computing (IMC) is a non-von Neumann paradigm that has recently established itself as a promising approach for energy-efficient, high throughput hardware for deep learning applications. One prominent application of IMC is that of performing matrix-vector multiplication in (1) time complexity by mapping the synaptic weights of a neural-network layer to the devices of an ... » read more

Neuromorphic electronics based on copying and pasting the brain


Abstract: "Reverse engineering the brain by mimicking the structure and function of neuronal networks on a silicon integrated circuit was the original goal of neuromorphic engineering, but remains a distant prospect. The focus of neuromorphic engineering has thus been relaxed from rigorous brain mimicry to designs inspired by qualitative features of the brain, including event-driven sign... » read more

Standards for the Characterization of Endurance in Resistive Switching Devices


Abstract "Resistive switching (RS) devices are emerging electronic components that could have applications in multiple types of integrated circuits, including electronic memories, true random number generators, radiofrequency switches, neuromorphic vision sensors, and artificial neural networks. The main factor hindering the massive employment of RS devices in commercial circuits is related to... » read more

Nanoporous Dielectric Resistive Memories Using Sequential Infiltration Synthesis


Abstract "Resistance switching in metal–insulator–metal structures has been extensively studied in recent years for use as synaptic elements for neuromorphic computing and as nonvolatile memory elements. However, high switching power requirements, device variabilities, and considerable trade-offs between low operating voltages, high on/off ratios, and low leakage have limited their utility... » read more

Modeling electrical conduction in resistive-switching memory through machine learning


Published in AIP Advances on July 13, 2021. Read the full paper (open access). Abstract Traditional physical-based models have generally been used to model the resistive-switching behavior of resistive-switching memory (RSM). Recently, vacancy-based conduction-filament (CF) growth models have been used to model device characteristics of a wide range of RSM devices. However, few have focused o... » read more

SMASH: Synchronized Many-sided Rowhammer Attacks from JavaScript


Authors: Finn de Ridder, ETH Zurich and VU Amsterdam; Pietro Frigo, Emanuele Vannacci, Herbert Bos, and Cristiano Giuffrida, VU Amsterdam; Kaveh Razavi, ETH Zurich Abstract: "Despite their in-DRAM Target Row Refresh (TRR) mitigations, some of the most recent DDR4 modules are still vulnerable to many-sided Rowhammer bit flips. While these bit flips are exploitable from native code, tri... » read more

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