A fully parallel approximate CORDIC design
Bibliographic record
Abstract
This paper proposes a new approximate scheme for a coordinate rotation digital computer (CORDIC) design; this scheme is based on modifying the existing Para-CORDIC architecture with multiple approximations. These approximations make possible a relaxation of the CORDIC algorithm itself, such that a fully parallel approximate CORDIC (FPAX-CORDIC) scheme is designed. This scheme avoids the memory register of Para-CORDIC and makes fully parallel the generation of the rotation direction. A comprehensive analysis and the evaluation of the error introduced by the approximations together with different circuit-related metrics are pursued using HSPICE as simulation tool. The error analysis of this paper combines existing figures of merit for approximate computing (such as the Mean Error Distance (MED)) with CORDIC-specific parameters; a good agreement between expected and simulated error values is found. As an application to image processing, the Discrete Cosine Transformation (DCT) is investigated by utilizing the proposed approximate FPAX-CORDIC architecture with different accuracy requirements. The results confirm the viability of the proposed scheme.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".