A Majority-based Approximate Adder for FPGAs
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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.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it