Approximate Restoring Dividers Using Inexact Cells and Estimation From Partial Remainders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Approximate computing can be used in error-resilient applications to reduce power consumption and increase overall circuit performance. This article introduces two approximate dividers with restoring array-based architecture that achieve substantial hardware savings while maintaining high accuracy when compared to existing approximate designs. The first design replaces exact restoring divider cells with a proposed approximate cell in a column-wise fashion. The second design uses several rows of exact architecture to compute a partial remainder and then rounds and encodes the divisor and this partial remainder so that they may be used to express approximate outputs. A comprehensive accuracy and performance evaluation are performed for the proposed dividers as well as other state-of-the-art designs. When compared to an exact design, the proposed dividers have a reduced area and power consumption of 46 and 57 percent respectively while introducing minimal error. Furthermore, the trade-off between accuracy and improved performance is explored for various approximate dividers in order to determine which designs achieve the best compromise. The accuracy of the proposed dividers is then demonstrated using two image processing applications.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it