A physical level study and optimization of CAM-based checkpointed register alias table
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Using full-custom layouts in 130 nm technology, this work studies how the latency and energy of a checkpointed, CAM-based Register Alias Table (cRAT) vary as a function of the window size, the issue width, and the number of embedded global checkpoints (GCs). These results are compared to those of the SRAM-based RAT (sRAT). Understanding these variations is useful during the early stages of architectural exploration where physical level information is not yet available. It is found that compared to sRAT, cRAT is more sensitive to the number of physical registers and issue width, however, it is less sensitive to the number of GCs. In addition, beyond a certain number of GCs, cRAT becomes faster than its equivalent sRAT. For instance, this is true when a RAT for 64 architectural and 128 physical registers has at least 20 GCs. This work also proposes an energy optimization for the cRAT; this optimization selectively disables cRAT entries that do not result in a match during lookup. The energy savings are, for the most part, a function of the number of physical registers. For instance, for a cRAT with 128 entries energy is reduced by 40%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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