Exploiting non-uniformity of write accesses for designing a high-endurance hybrid Last Level Cache in 3D CMPs
Bibliographic record
Abstract
In chip-multiprocessors with increasing the number of cores, power consumption becomes the main concern in Last Level Cache (LLC). Emerging technologies, such as three-dimensional integrated circuits (3D ICs) and non-volatile memories (NVMs) are among the newest solutions to the design of dark-silicon-aware multi/many-core systems. Although NVMs have many advantages like low leakage and high density, they suffer from shortcomings such as the limited number of write operations and long write operation latency and high energy. In this paper, we use the non-uniform distribution of the accesses and the writes in banks of LLC to improve the lifetime of NVM in LLC and decrease energy consumption. We propose a new hybrid cache design that consists of SRAM banks and STT-RAM banks. Experimental results show that the proposed method improves the energy-delay product by about 43% on average under PARSEC workloads execution. Moreover, this technique improves performance by about 7% on average compared to the conventional methods with STT-RAM cache technology under PARSEC workloads execution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".