An Adaptive Sleep Transistor Biasing Scheme for Low Leakage SRAM
Bibliographic record
Abstract
Reducing the leakage power in embedded SRAM memories is critical for low-power applications. Raising the source voltage of SRAM cells in standby mode reduces the leakage currents effectively. However, in order to preserve the state of the cell in standby mode, source voltage cannot be raised beyond a certain level. The maximum source voltage of an SRAM cell is determined by its hold stability in a particular process corner. Hence, in order to achieve the maximum leakage reduction, the source voltage of each individual cell must be raised up to its maximum safe level. However, any cell-based technique realizing this would be practically not feasible. In this paper, we propose an SRAM leakage reduction technique, referred to as adaptive sleep transistor biasing, which automatically fine-tunes the source voltage of individual memory blocks to their optimum level. Thus, maximum leakage savings can be expected while data is safely retained during standby mode. Preliminary study shows that the proposed scheme has the potential of providing substantial saving in leakage power over those by using the conventional techniques.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".