Pj-AxMTJ: Process-in-memory with Joint Magnetization Switching for Approximate Computing in Magnetic Tunnel Junction
Bibliographic record
Abstract
In order to realize high efficient magnetization switching in magnetic tunnel junction (MTJ), several potential alternative mechanisms have been realized to replace spin transfer torque (STT) method, such as the STT-assisted precessional voltage controlled magnetic anisotropy (VCMA), and the spin orbit torque (SOT) erasing plus STT programing. In this paper, we propose a method denoted as the Process-in-memory with Joint magnetization switching for Approximate computing in Magnetic Tunnel Junction (Pj-AxMTJ), by using the 1T-1M and 3T-1M bit-cell structures. The proposed method aims to implement a low-precision computational memory with dynamic approximate computing. Specifically, four nonvolatile approximate full adders (AxFAs) are proposed based on the writing operations of different types of magnetic random access memory. As no peripheral circuits but the memory bit-cells are used in the proposed design, the resultant area is significantly small. Moreover, the AxFAs can be easily reconfigured into memory units with simple wire connections. The simulation results for the proposed designs are then presented to show the precision-power-area-speed tradeoffs for addition operation. Finally, the accuracy of the AxFAs are further evaluated in an image processing application.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".