An approach for integrating basic retiming and software pipelining
Bibliographic record
Abstract
Basic retiming is an algorithm originally developed for hardware optimization. Software pipelining is a technique proposed to increase instruction-level parallelism for parallel processors. In this paper, we show that applying software pipelining alone for minimizing timings under resource constraints can lead to sub-optimal results, compared to the case if an unification of basic retiming and software pipelining is used. We propose an approach to realize this unification. The approach allows to minimize the code size of the optimized loop as well as minimizing the idleness of computational elements. We extend this approach to solve the problem of minimizing peak power consumption for time-constrained and resource-constrained software pipelined loops. Solving these problems is important for portable embedded systems as well as system-on-chip design. The approaches are tested using known benchmarks. On average, relative timing improvement is 60.19%, and relative reduction of peak power consumption is 13.17% without any trade-off in timings.
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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".