Efficient Approximate Decomposition Solver using Ising Model
Bibliographic record
Abstract
Computing with memory is an energy-efficient computing approach. It pre-computes a function and stores its values in a lookup table (LUT), which can be retrieved at runtime. Approximate Boolean decomposition reduces the LUT size for implementing complex functions, but it takes a long time to find a decomposition with a minimal error. In this work, to address this issue, we propose an efficient Ising model-based approximate Boolean decomposition solver. First, a new column-based approximate disjoint decomposition method is proposed to fit the Ising model. Then, it is adapted to the Ising model-based optimization solver. Moreover, two improvement techniques are developed for an efficient search of the approximate disjoint decomposition when using simulated bifurcation to solve the Ising model. Experimental results show that compared to the state-of-the-art work, our approach achieves a 11% smaller mean error distance with an average 1.16× speedup when approximately decomposing 16-input Boolean functions.
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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 | 0.000 | 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 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".