Fast Design Space Exploration Using Local Regression Modeling With Application to ASIPs
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
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%.
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.
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 | 0.001 |
| Science and technology studies | 0.001 | 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 it