Point Estimation in Design Space Exploration Using Local Regression Modeling
Bibliographic record
Abstract
Configuration of an application-specific instruction-set processor (ASIP) through an exhaustive search of the design space is computationally prohibitive. To enable further automation, new methods are needed to speed up design space exploration (DSE), since the evaluation of each configuration is very expensive in terms of run-time. One method of speeding up DSE is to simulate a small sample of the design space and then use this information to model the rest of the design space using statistical regression techniques. From this model, unknown points within the space can be estimated. This approach has the potential to speed-up DSE time by several orders of magnitude. In this paper, we study the effectiveness of using local regressions statistics (LOESS) to model the design space. We compare the use of a non-parametric statistics based on LOESS to polynomial regressions in their ability to estimate unknown points. After showing the effectiveness of LEOSS, we apply it to the configuration of the pattern history table (PHT) of a branch predictor when configured to minimize the overall power dissipation of the processor.
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 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 | 0.001 | 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 | 0.001 |
| 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".