A Low Power and High Sensing Margin Non-Volatile Full Adder Using Racetrack Memory
Bibliographic record
Abstract
The continuing miniaturization of complementary metal oxide semiconductor (CMOS) technology has brought in two critical issues-the high power and long global interconnection delay. Magnetic tunnel junction (MTJ) nanopillar with the advantages of non-volatility, fast switching speed, and high density promises new designs and architectures to significantly alleviate the power and delay issues. This paper presents a new design of the key component in processors-multi-bit full adder, whose input and output data are stored in perpendicular magnetic anisotropy (PMA) domain wall (DW) racetrack memory (RTM). The MTJ sharing technique with demultiplexing approach is used in the proposed non-volatile full adder (NVFA) to greatly reduce the area and power, and improve the speed and sensing margin as well. The proposed NVFA scheme can also apply to the other types of non-volatile memory (NVM). Compared to the state-of-art magnetic full adder (MFA), our proposed NVFA has reduced the power and area by 5.9 times and 50%, respectively. It also accelerates the speed by 10% and increases the sensing margin by more than 66%.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".