Bibliographic record
Abstract
With continuous and aggressive technology scaling, suppressing the stand-by power is among the top priorities for SRAM design. Switching off the less-frequently accessed blocks is an efficient way to reduce the stand-by power, provided that the information stored in these blocks can be restored. Non-volatile memories (NVMs) are integrated into SRAM cells to perform the required store and restore functions. Among various types of NVMs, memristors (a.k.a. RRAM) have several advantages including their small device size, low voltage operation, high speed, and CMOS-compatible fabrication process. In this article, we propose a new 8T1R RRAM-based non-volatile SRAM (NV-SRAM) which adds non-volatility to the SRAM with minimum impact on the Write and Read operations. Simulation at cell-level and array-level have confirmed that the new design performs Read and Write operations at a compatible delay, energy and noise margin as the conventional 6T SRAM, and it is among the best of all reported RRAM-based NV-SRAM designs to our knowledge. In addition, since our 8T1R design uses only one RRAM device per cell, the energy required for storing/restoring the SRAM data to/from the RRAM is significantly reduced by 60%/70% compared to the lowest storing/restoring energy of the previously proposed RRAM-based NV-SRAM designs.
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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.035 | 0.025 |
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".