Reordering the assembly instructions in basic blocks to reduce switching activities on the instruction bus
Bibliographic record
Abstract
Execution time is no longer the only target to achieve when designing programmes for today and next-generation CMOS-based digital systems. One needs to also consider reducing power dissipation. Buses contribute to the power dissipation during the execution of a given programme since instructions and/or operands have to be fetched from the memory. Reducing power dissipation in buses has been addressed in the literature. In this study, the authors address the problem of reducing power dissipation of the instruction bus by reordering the instructions in basic blocks without increasing the executing time and the code size, and while maintaining the original functionality of the programme. The authors target embedded processors having Harvard architecture. They focus on solving this problem for programmes developed at the assembly level, since at that level the machine code can be obtained by simply running an assembler, which allows an accurate computation of switching activities on the instruction bus by considering each pair of instructions. The authors formulate this problem as an integer linear programme (ILP), and they provide two heuristics. Experimental results have shown that the proposed approach can reduce switching activities. The ILP has reduced switching activities by as high as 38%. One of the two proposed heuristics has always resulted in reducing switching activities, and its relative savings are within an average of 5% from the optimum produced using the ILP.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".