On the Latency and Energy of Checkpointed Superscalar Register Alias Tables
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it