| pLUTo: Enabling Massively Parallel Computation in DRAM via Lookup Tables | 0 | 0.34 | 2022 |
| A Deeper Look into RowHammer’s Sensitivities: Experimental Analysis of Real DRAM Chips and Implications on Future Attacks and Defenses | 3 | 0.37 | 2021 |
| CODIC: A Low-Cost Substrate for Enabling Custom In-DRAM Functionalities and Optimizations | 2 | 0.35 | 2021 |
| Uncovering In-DRAM RowHammer Protection Mechanisms:A New Methodology, Custom RowHammer Patterns, and Implications | 2 | 0.36 | 2021 |
| BurstLink: Techniques for Energy-Efficient Video Display for Conventional and Virtual Reality Systems | 0 | 0.34 | 2021 |
| IChannels: Exploiting Current Management Mechanisms to Create Covert Channels in Modern Processors | 2 | 0.35 | 2021 |
| BlockHammer: Preventing RowHammer at Low Cost by Blacklisting Rapidly-Accessed DRAM Rows | 1 | 0.34 | 2021 |
| QUAC-TRNG: High-Throughput True Random Number Generation Using Quadruple Row Activation in Commodity DRAM Chips | 7 | 0.37 | 2021 |
| GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis | 1 | 0.34 | 2020 |
| SysScale: Exploiting Multi-domain Dynamic Voltage and Frequency Scaling for Energy Efficient Mobile Processors | 3 | 0.35 | 2020 |
| Accelerating Genome Analysis: A Primer on an Ongoing Journey | 11 | 0.46 | 2020 |
| FIGARO: Improving System Performance via Fine-Grained In-DRAM Data Relocation and Caching | 2 | 0.35 | 2020 |
| FlexWatts: A Power- and Workload-Aware Hybrid Power Delivery Network for Energy-Efficient Microprocessors | 0 | 0.34 | 2020 |
| Bit-Exact ECC Recovery (BEER): Determining DRAM On-Die ECC Functions by Exploiting DRAM Data Retention Characteristics | 4 | 0.37 | 2020 |
| Are We Susceptible to Rowhammer? An End-to-End Methodology for Cloud Providers | 10 | 0.45 | 2020 |
| RowHammer: A Retrospective. | 17 | 0.57 | 2020 |
| Apollo: a sequencing-technology-independent, scalable and accurate assembly polishing algorithm | 3 | 0.37 | 2019 |
| Understanding and Modeling On-Die Error Correction in Modern DRAM: An Experimental Study Using Real Devices | 7 | 0.41 | 2019 |
| Processing-in-Memory: A Workload-Driven Perspective | 19 | 0.68 | 2019 |
| Dataplant: In-DRAM Security Mechanisms for Low-Cost Devices. | 0 | 0.34 | 2019 |
| D-RaNGe: Using Commodity DRAM Devices to Generate True Random Numbers with Low Latency and High Throughput | 18 | 0.43 | 2019 |
| CROW: a low-cost substrate for improving DRAM performance, energy efficiency, and reliability | 12 | 0.42 | 2019 |
| Solar-DRAM: Reducing DRAM Access Latency by Exploiting the Variation in Local Bitlines | 6 | 0.36 | 2018 |
| The DRAM Latency PUF: Quickly Evaluating Physical Unclonable Functions by Exploiting the Latency-Reliability Tradeoff in Modern Commodity DRAM Devices | 17 | 0.45 | 2018 |
| D-RaNGe: Violating DRAM Timing Constraints for High-Throughput True Random Number Generation using Commodity DRAM Devices. | 0 | 0.34 | 2018 |
| FLIN: Enabling Fairness and Enhancing Performance in Modern NVMe Solid State Drives. | 7 | 0.46 | 2018 |
| Reducing DRAM Latency via Charge-Level-Aware Look-Ahead Partial Restoration. | 9 | 0.39 | 2018 |
| The Reach Profiler (REAPER): Enabling the Mitigation of DRAM Retention Failures via Profiling at Aggressive Conditions. | 21 | 0.51 | 2017 |
| Ambit: in-memory accelerator for bulk bitwise operations using commodity DRAM technology. | 73 | 1.75 | 2017 |
| RowHammer: Reliability Analysis and Security Implications. | 6 | 0.38 | 2016 |
| Buddy-RAM: Improving the Performance and Efficiency of Bulk Bitwise Operations Using DRAM. | 0 | 0.34 | 2016 |
