Bibliographic record
Abstract
Posit number system has been recently widely applied in many fields of applications. For different applications, the precision requirements are usually different. In addition, the transprecision computing paradigm, which is proposed for energy efficient computation, even requires different precision in each computation step. To support computations of various precision in a single hardware architecture, in this paper, a unified architecture of multiple-precision posit multiplier is proposed. The proposed posit multiplier supports the commonly used Posit(8, 0), Posit(16, 1), and Posit(32, 2) formats, where one Posit(32, 2), or two parallel Posit(16, 1), or four parallel Posit(8, 0) multiplications can be accomplished each time. Each module of the proposed posit multiplier is carefully tailored for resource sharing among three supported precision formats. Compared to the Posit(32, 2) multiplier, the proposed multiple- precision multiplier adds the support for parallel low-precision posit multiplications with only 12.8% more area and 15.4% more power. The proposed architecture can be used in posit-enabled general-purpose processor 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".