Demo Data from EDBT 2026 / reviewers in the wild / expert
Sangkyu Yoon
dblp:198/3983
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1
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 · 100% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Memory systems
DRAM |
0.3 | 1 | 2017 | Retention-Aware Hybrid Main Memory (RAHMM): Big DRAM and Little SCM · IEEE Trans. Computers 2017 |
Memory systems › DRAM
DRAM refresh |
0.3 | 1 | 2017 | Retention-Aware Hybrid Main Memory (RAHMM): Big DRAM and Little SCM · IEEE Trans. Computers 2017 |
Memory systems
hybrid memory |
0.3 | 1 | 2017 | Retention-Aware Hybrid Main Memory (RAHMM): Big DRAM and Little SCM · IEEE Trans. Computers 2017 |
Memory systems
main memory |
0.3 | 1 | 2017 | Retention-Aware Hybrid Main Memory (RAHMM): Big DRAM and Little SCM · IEEE Trans. Computers 2017 |
Memory systems › DRAM › DRAM refresh
retention-aware refresh |
0.3 | 1 | 2017 | Retention-Aware Hybrid Main Memory (RAHMM): Big DRAM and Little SCM · IEEE Trans. Computers 2017 |
Memory systems
non-volatile memory |
0.1 | 1 | 2017 | Retention-Aware Hybrid Main Memory (RAHMM): Big DRAM and Little SCM · IEEE Trans. Computers 2017 |
Memory systems › non-volatile memory
storage class memory |
0.1 | 1 | 2017 | Retention-Aware Hybrid Main Memory (RAHMM): Big DRAM and Little SCM · IEEE Trans. Computers 2017 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Retention-Aware Hybrid Main Memory (RAHMM): Big DRAM and Little SCMabstractHybrid memory comprised of a big SCM and a little DRAM (BSLD) is widely studied to address the growing power consumption challenge of pure DRAM. However, the performance degradation, limited endurance and immature mass production of ultra-high-density SCM are still the painful points of BSLD. Here we propose a Retention-Aware Hybrid Main Memory (RAHMM) architecture with a big DRAM and a little SCM (BDLS) for the first time. DRAM is refreshed at a much longer interval by using SCM to store the small quantity of leaky tail bits in DRAM. A two-step search technology combined with outcome forecasting is put forward to get ultra-fast read access, as well as to diminish the power and performance overheads. A hidden buffer strategy (HBS) is proposed to optimize write performance and endurance hurt. The experimental results show 45 percent reduction of power consumption and 30 percent performance optimization, which are significantly improved compared to that of both serial and parallel BSLD with a counterpart capacity Weiliang Jing, Yinyin Lin, Beomseop Lee, Sangkyu Yoon, Yuan Du, Bomy Chen |
IEEE Trans. Computers | 5 |