Methods for optimizing register placement in synchronous circuits derived using software pipelining techniques
Bibliographic record
Abstract
A method based on software pipelining has been recently proposed to optimize mono-phase clocked sequential circuits. The resulting circuits are multi-phase clocked sequential circuits, where all clocks have the same period. To preserve functionality of the original circuit, registers must be placed according to a correct schedule. This schedule also ensures the maximum throughput. In that method, it is question of (1) how to determine a schedule that requires the minimum number of registers, and (2) how to place these registers optimally. In this paper, problems (1) and (2) are tackled simultaneously. More precisely, we deal with the problem of determining schedules with the minimum register requirements, where the optimal register placement is done during the schedule determination. To optimally solve that problem, we provide a mixed integer linear program that we use to derive a linear program, which is polynomial-time solvable. We show that the dual of this linear program can be transformed to a minimum cost network flow problem, which can be solved more efficiently. Experimental results confirm the effectiveness of the approach, and show that significant reductions of the number of registers can be obtained. Also, they confirm that the obtained dual formulation can be solved much faster than its primal.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".