DPALS: A dynamic programming-based algorithm for two-level approximate logic synthesis
Bibliographic record
Abstract
Approximate circuit design is an emerging paradigm in which a designer deliberately changes the specified Boolean function to reduce area, delay, and/or power consumption of a circuit. This paper focuses on the synthesis of approximate logic circuits (or ALS) under a given error constraint. In particular, we consider ALS for a two-level design under an error rate constraint. A dynamic programming-based algorithm is proposed to find a nearly optimal approximate function by identifying the most promising set of cubes to be added to the on-set of the original function. Then, an off-the-shelf two-level logic synthesis tool is applied to further optimize the sum-of-product (SOP) expression. The experimental results show that the literal reduction is close to the optimal solution when the error rate constraint is tight and that more than 50% literal reduction is achieved for error rate below 0.8% for an 8-bit adder and a square root circuit.
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How this classification was reachedexpand
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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".