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Guoming Rao

dblp:351/3572 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Memory systems
DRAM
1.012026
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
1.012026
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
0.312026
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
0.312026
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
YearPublicationVenuePosition
2026 TPDA-DRAM: A Variation-Aware DRAM Improving System Performance via In-Situ Timing Margin Detection and Adaptive Mitigation
abstract
DRAM 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. Computers3