Microarchitectural innovations: boosting microprocessor performance beyond semiconductor technology scaling
Bibliographic record
Abstract
Semiconductor technology scaling provides faster and more plentiful transistors to build microprocessors, and applications continue to drive the demand for more powerful microprocessors. Weaving the "raw" semiconductor material into a microprocessor that offers the performance needed by modern and future applications is the role of computer architecture. This paper overviews some of the microarchitectural techniques that empower modem high-performance microprocessors. The techniques are classified into: 1) techniques meant to increase the concurrency in instruction processing, while maintaining the appearance of sequential processing and 2) techniques that exploit program behavior. The first category includes pipelining, superscalar execution, out-of-order execution, register renaming, and techniques to overlap memory-accessing instructions. The second category includes memory hierarchies, branch predictors, trace caches, and memory-dependence predictors. The paper also discusses microarchitectural techniques likely to be used in future microprocessors, including data value speculation and instruction reuse, microarchitectures with multiple sequencers and thread-level speculation, and microarchitectural techniques for tackling the problems of power consumption and reliability.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".