A survey on bit dimension optimization strategies of microprograms
Bibliographic record
Abstract
Microprogram optimization is one way to increase efficiency, and optimization can be crucial in some applications. Optimization refers to a reduction of execution time of microprograms, or of the control store size, B*W, where W represents the word dimension of the control store which is the number of words of control store required for certain application, and B represents the bit dimension which is the number of bits per word of control store. The various optimization strategies can be broadly classified under four categories: bit dimension reduction, word dimension reduction, state reduction, and heuristic reduction. A survey of the various bit dimension optimization techniques has been presented by Agerwala in his 1976 paper, where the techniques are critically analyzed and compared, and the results of analysis are discussed. The paper further augments the work of Agerwala, taking into account the optimization methods developed later and hence not discussed by him. Also, the present study considers the optimization problem in the case of polyphase microinstructions in addition to that for monophase microinstructions. The prospective, current status, and future trends in this direction are also briefly outlined.>
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".