Bibliographic record
Abstract
We introduce the Coarse-Grain Out-of-Order (CG-OoO) general-purpose processor designed to achieve close to In-Order (InO) processor energy while maintaining Out-of-Order (OoO) performance. CG-OoO is an energy-performance-proportional architecture. Block-level code processing is at the heart of this architecture; CG-OoO speculates, fetches, schedules, and commits code at block-level granularity. It eliminates unnecessary accesses to energy-consuming tables and turns large tables into smaller, distributed tables that are cheaper to access. CG-OoO leverages compiler-level code optimizations to deliver efficient static code and exploits dynamic block-level and instruction-level parallelism. CG-OoO introduces Skipahead, a complexity effective, limited out-of-order instruction scheduling model. Through the energy efficiency techniques applied to the compiler and processor pipeline stages, CG-OoO closes 62% of the average energy gap between the InO and OoO baseline processors at the same area and nearly the same performance as the OoO. This makes CG-OoO 1.8× more efficient than the OoO on the energy-delay product inverse metric. CG-OoO meets the OoO nominal performance while trading off the peak scheduling performance for superior energy efficiency.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".