Bibliographic record
Abstract
Posit number system has been used as an alternative to IEEE floating-point number system in many applications, especially the recent popular deep learning. Its non-uniformed number distribution fits well with the data distribution of deep learning and thus can speedup the training process of deep learning. Among all the related arithmetic operations, multiplication is one of the most frequent operations used in applications. However, due to the bit-width flexibility nature of posit numbers, the hardware multiplier is usually designed with the maximum possible mantissa bit-width. As the mantissa bit-width is not always the maximum value, such multiplier design leads to a high power consumption especially when the mantissa bit-width is small. In this brief, a power efficient posit multiplier architecture is proposed. The mantissa multiplier is still designed for the maximum possible bit-width, however, the whole multiplier is divided into multiple smaller multipliers. Only the required small multipliers are enabled at run-time. Those smaller multipliers are controlled by the regime bit-width which can be used to determine the mantissa bit-width. This design technique is applied to 8-bit, 16-bit, and 32-bit posit formats in this brief and an average of 16% power reduction can be achieved with negligible area and timing overhead.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.010 | 0.003 |
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".