On the Latency and Energy of Checkpointed Superscalar Register Alias Tables
Bibliographic record
Abstract
This paper investigates how the latency and energy of register alias tables (RATs) vary as a function of the number of global checkpoints (GCs), processor issue width, and window size. It improves upon previous RAT checkpointing work that ignored the actual latency and energy tradeoffs and focused solely on evaluating performance in terms of instructions per cycle (IPC). This work utilizes measurements from the full-custom checkpointed RAT implementations developed in a commercial 130-nm fabrication technology. Using physical- and architectural-level evaluations together, this paper demonstrates the tradeoffs among the aggressiveness of the RAT checkpointing, performance, and energy. This paper also shows that, as expected, focusing on IPC alone incorrectly predicts performance. The results of this study justify checkpointing techniques that use very few GCs (e.g., four). Additionally, based on full-custom implementations for the checkpointed RATs, this paper presents analytical latency and energy models. These models can be useful in the early stages of architectural exploration where actual physical implementations are unavailable or are hard to develop. For a variety of RAT organizations, our model estimations are within 6.4% and 11.6% of circuit simulation results for latency and energy, respectively. This range of accuracy is acceptable for architectural-level studies.
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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 | 0.009 |
| 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 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".