Fast Power Estimation for Automatic Instruction-Set Selection
Bibliographic record
Abstract
Recent research in the area of application specific instruction-set processors (ASIPs) has focused on the automatic selection of a custom instruction-set based on a high-level description of the application. Automatic instruction-set selection is typically comprised of instruction selection and instruction enumeration. During instruction enumeration, candidate instructions are identified using a simple cost function that minimizes the total number of operations in each basic block of the application while also adhering to the micro-architectural constraints of the ASIP. Existing methods indirectly account for power by using the above mentioned cost function and relying on the assumption that fewer operations will always reduce power. This approach is generally taken because power estimation is time-consuming. In this paper, we directly estimate the power dissipation of a custom instruction by using a simple yet effective probabilistic approach based on probability distributions of the input Hamming distance. Results indicate that our approach can estimate the power dissipation incurred by a custom instruction to within 12% of the value reported by PrimePower.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".