A Memristive Multiplier Using Semi-Serial IMPLY-Based Adder
Bibliographic record
Abstract
Memristors are among emerging technologies with many promising features, which makes them suitable not only for storage purposes but also for computations. In this work, focusing on in-memory computations, we first present our semi-serial IMPLY-based adder and perform an extensive analysis of its merits. In addition to providing a favorable balance between the number of steps and number of memristors, a key property of the presented adder is its compactness as compared to the state-ofthe-art adders. Next, using our semi-serial adder, we propose an IMPLY-based multiplier. We show that the proposed multiplier is more than 5× better than other works based on the figure of merit which gives equal weight to the number of steps (i.e., speed) and required die area. Additionally, we provide a deeper insight into IMPLY-based arithmetic units, their properties, design characteristics, and advantages or disadvantages compared to one another by proposing new figures of merit and performing comprehensive comparative analyses. This facilitates the process of design, or selection, of suitable units for the design engineers and researchers in the field.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".