Demo Data from VLDB 2026 / reviewers in the wild / expert
Guoming Rao
dblp:351/3572
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
0009-0001-1544-9084ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Memory systems · 50% Processor architecture and microarchitecture · 38% Energy-efficient computing · 12% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Memory systems
DRAM |
1.0 | 1 | 2026 | TPDA-DRAM: A Variation-Aware DRAM Improving System Performance via In-Situ Timing Margin Detection and Adaptive Mitigation · IEEE Trans. Computers 2026 |
Processor architecture and microarchitecture › adaptive architecture
timing margin adaptation |
1.0 | 1 | 2026 | TPDA-DRAM: A Variation-Aware DRAM Improving System Performance via In-Situ Timing Margin Detection and Adaptive Mitigation · IEEE Trans. Computers 2026 |
Energy-efficient computing › memory energy efficiency
DRAM energy efficiency |
0.3 | 1 | 2026 | TPDA-DRAM: A Variation-Aware DRAM Improving System Performance via In-Situ Timing Margin Detection and Adaptive Mitigation · IEEE Trans. Computers 2026 |
Memory systems › memory access latency
DRAM latency |
0.3 | 1 | 2026 | TPDA-DRAM: A Variation-Aware DRAM Improving System Performance via In-Situ Timing Margin Detection and Adaptive Mitigation · IEEE Trans. Computers 2026 |
Methods — techniques the papers use, named apart from their topics
in-situ cross-coupled detectors · 1.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | TPDA-DRAM: A Variation-Aware DRAM Improving System Performance via In-Situ Timing Margin Detection and Adaptive MitigationabstractDRAM latency remains a critical bottleneck in the performance of modern computing systems. However, the latency is excessively conservative due to the timing margins imposed by DRAM vendors to accommodate rare worst-case scenarios, such as weak cells and high temperatures. In this study, we introduce a temperature- and process-variation-aware timing detection and adaptation DRAM (TPDA-DRAM) architecture that dynamically mitigates timing margins at runtime. TPDA-DRAM leverages innovativein-situcross-coupled detectors to monitor voltage differences between bitline pairs inside DRAM arrays, ensuring precise detection of timing margins. Additionally, the proposed detector inherently accelerates the precharge operation of DRAM, thereby reducing the precharge latency by up to 62.5%. Building upon this architecture, we propose two variation-aware timing adaptation schemes: 1) a process-variation-aware adaptation (PVA) scheme that accelerates access to weak cells, mitigating process-induced timing margins, and 2) a temperature-variation-aware adaptation (TVA) scheme that leverages temperature information and the restoration truncation technique to reduce DRAM latency, mitigating temperature-induced timing margins. Evaluations on an eight-core computing system show that TPDA-DRAM improves average performance by 21.8% and energy efficiency by 18.2%. Yuxuan Qin, Chuxiong Lin, Guoming Rao, Weiguang Sheng, Weifeng He |
IEEE Trans. Computers | 3 |