A recursive technique for computing lower-bound performance of schedules
Bibliographic record
Abstract
Presents a fast recursive technique for estimating a lower-bound performance of data path schedules. The method relies on the determination of an ASAPUC (as soon as possible under constraint) time-step value for the root of the DFG (data flow graph) that is based on the ASAPUC values of its predecessor nodes, etc., until the leaf nodes are reached where this value becomes the regular ASAP value. The method computes a tighter lower-bound than the greedy technique and is only two times slower on the same benchmarks. Synthesis methods that depend on the exploration of the solution space directed by a lower-bound estimation, such a local microcode generation and behavioral synthesis, can benefit from our method. This is because bad solutions can be pruned earlier. We illustrate this dramatic effect on the reduction of the search space during the synthesis of an optimal microcode sequence for the elliptic wave filter benchmark and a fixed data path (containing a multiport RAM and a ROM).>
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".