Fast Design Space Exploration Using Local Regression Modeling With Application to ASIPs
Bibliographic record
Abstract
The configuration of an application-specific instruction-set processor through an exhaustive search of the design space is computationally prohibitive. Consequently, we propose a novel algorithm that models the design space using local regression statistics. With only a small subset of the design space sampled, our model uses statistical inference to estimate all remaining points. This technique enables existing design space exploration approaches to make longer strides toward the optimal point while evaluating fewer points in the design space. We tested our approach on two important aspects of processor architecture. Initially, we optimized the pattern history table (PHT) of a GSelect branch predictor to minimize the total energy of an embedded processor. Our approach was able to find the optimal configuration for the majority of benchmarks tested. By configuring the PHT size using our approach, the total processor energy was reduced by 17.2% on average, which is close to the possible percentage of 17.6% using optimal configurations. We then extended our approach to a multidimensional cache tuning problem where we configured a two-level cache hierarchy with 19 278 possible configurations. In this case, only 1% of the design space was simulated, resulting in a 100 times speedup. In doing so, we were able to identify near optimal configurations for most benchmarks and reduce the overall energy of the processor by 13.9% on average, with one benchmark by as much as 53%.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".