Power Awareness through Selective Dynamically Optimized Traces
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
We present the PARROT concept that seeks to achievehigher performance with reduced energy consumptionthrough gradual optimization of frequently executed codetraces. The PARROT microarchitectural framework integratestrace caching, dynamic optimizations and pipelinedecoupling. We employ a selective approach for applyingcomplex mechanisms only upon the most frequently usedtraces to maximize the performance gain at any givenpower constraint, thus attaining finer control of tradeoffsbetween performance and power awareness.We show that the PARROT based microarchitecture canimprove the performance of aggressively designed processorsby providing the means to improve the utilizationof their more elaborate resources. At the same time, rigorousselection of traces prior to storage and optimizationprovides the key to attenuating increases in thepower budget.For resource-constrained designs, PARROT based architecturesdeliver better performance (up to an average16% increase in IPC) at a comparable energy level,whereas the conventional path to a similar performanceimprovement consumes an average 70% more energy.Meanwhile, for those designs which can tolerate a higherpower budget, PARROT gracefully scales up to use additionalexecution resources in a uniformly efficient manner.In particular, a PARROT-style doubly-wide machinedelivers an average 45% IPC improvement while actuallyimproving the cubic-MIPS-per-WATT power awarenessmetric by over 50%.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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