Model-based optimization of High Level Synthesis directives
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
High Level Synthesis (HLS) tools improve the speed of FPGA hardware design entry compared to traditional hardware description languages by raising the level of design abstraction. Using compiler directives to guide the tool, a wide variety of hardware architectures can be obtained without modification of the original behavioural code. However, selecting an optimal application of directives from this large design space can be daunting and time-consuming for a designer since evaluating a particular setting of directives requires running the FPGA tool flow. This work considers the use of sequential model-based optimization (SMBO) methods for automatically selecting directive settings. These methods construct models of the design space to guide the optimization process and minimize the number of tool evaluations. In this paper, we evaluate the use of SMBO for selecting HLS directives and extend the method to relate multiple uses of the same directive within a design. We observe that SMBO can quickly find optimal directive settings in a space of tens of thousands of possible directive configurations and find that our proposed extension can further improve the convergence rate over the standard method.
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 | 0.000 | 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 it