EDBT 2026 Demo / reviewers in the wild / expert
Devesh Singh
dblp:178/0183
· DBLP profile ↗
2ranked-venue papers
1as first author
2since 2021 · last 2024
0009-0003-5345-4655ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 2 · 1 first-author · 2 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 · 81% Integrated circuit design · 19% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Memory systems
cache |
0.5 | 1 | 2021 | Monolithically Integrating Non-Volatile Main Memory over the Last-Level Cache · ACM Trans. Archit. Code Optim. 2021 |
Memory systems › memory hierarchy › cache hierarchy
last-level cache |
0.5 | 1 | 2021 | Monolithically Integrating Non-Volatile Main Memory over the Last-Level Cache · ACM Trans. Archit. Code Optim. 2021 |
Integrated circuit design
monolithic integration |
0.5 | 1 | 2021 | Monolithically Integrating Non-Volatile Main Memory over the Last-Level Cache · ACM Trans. Archit. Code Optim. 2021 |
Memory systems › non-volatile memory
non-volatile main memory |
0.5 | 1 | 2021 | Monolithically Integrating Non-Volatile Main Memory over the Last-Level Cache · ACM Trans. Archit. Code Optim. 2021 |
Memory systems
non-volatile memory |
0.5 | 1 | 2021 | Monolithically Integrating Non-Volatile Main Memory over the Last-Level Cache · ACM Trans. Archit. Code Optim. 2021 |
Memory systems
processing-in-memory |
0.1 | 1 | 2021 | Monolithically Integrating Non-Volatile Main Memory over the Last-Level Cache · ACM Trans. Archit. Code Optim. 2021 |
Methods — techniques the papers use, named apart from their topics
simulation · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | MORSE: Memory Overwrite Time Guided Soft Writes to Improve ReRAM Energy and EnduranceabstractReRAM is an attractive main memory technology due to its high density and low idle power. However, ReRAM exhibits costly writes, especially in terms of energy and endurance. Prior device studies show that retention can be traded off for write energy and endurance by employing soft write operations with lower currents. But given their reduced retention times, soft writes require refresh operations to prevent data loss. Unfortunately, a large number of refreshes are needed in between writes to infrequently updated data. Hence, a non-volatile memory system with soft writes still needs traditional hard writes, and a way to choose between them. Devesh Singh, Donald Yeung |
PACT | 1 |
| 2021 | Monolithically Integrating Non-Volatile Main Memory over the Last-Level CacheabstractMany emerging non-volatile memories are compatible with CMOS logic, potentially enabling their integration into a CPU’s die. This article investigates such monolithically integrated CPU–main memory chips. We exploit non-volatile memories employing 3D crosspoint subarrays, such as resistive RAM (ReRAM), and integrate them over the CPU’s last-level cache (LLC). The regular structure of cache arrays enables co-design of the LLC and ReRAM main memory for area efficiency. We also develop a streamlined LLC/main memory interface that employs a single shared internal interconnect for both the cache and main memory arrays, and uses a unified controller to service both LLC and main memory requests. We apply our monolithic design ideas to a many-core CPU by integrating 3D ReRAM over each core’s LLC slice. We find that co-design of the LLC and ReRAM saves 27% of the total LLC–main memory area at the expense of slight increases in delay and energy. The streamlined LLC/main memory interface saves an additional 12% in area. Our simulation results show monolithic integration of CPU and main memory improves performance by 5.3× and 1.7× over HBM2 DRAM for several graph and streaming kernels, respectively. It also reduces the memory system’s energy by 6.0× and 1.7×, respectively. Moreover, we show that the area savings of co-design permits the CPU to have 23% more cores and main memory, and that streamlining the LLC/main memory interface incurs a small 4% performance penalty. Candace Walden, Devesh Singh, Meenatchi Jagasivamani, Shang Li 0001, Luyi Kang, Mehdi Asnaashari, Sylvain Dubois, Bruce L. Jacob, Donald Yeung |
ACM Trans. Archit. Code Optim. | 2 |