A Majority-based Approximate Adder for FPGAs
Bibliographic record
Abstract
The most advanced ASIC-based approximate adders are focused on gate or transistor level approximating structures. However, due to architectural differences between ASIC and FPGA, comparable performance gains for FPGA-based approximate adders cannot be obtained using ASIC-based approximation ones. In this paper, we propose a method for designing a low-error approximate adder that effectively deploys the modern FPGA structure. We introduce an FPGA-based approximate adder, named as Majority Approximate Adder (MAA), with less error than the advanced approximate adders. MAA is constructed using an approximate part and an accurate one; i.e. the accurate part is based on a smaller carry-chain compared with the carry-chain of the corresponding accurate adder. In addition, approximate part is designed to use FPGA resources efficiently with a low mean error distance (MED). Experimental results based on Monte-Carlo simulation demonstrates that a 16-bit MAA has a 49.92% lower MED than the state of the art FPGA-based approximate adder. MAA also takes up less area and consumes less power than other FPGA-based approximate adders in the literature.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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 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".