Sensei: An area-reduction advisor for FPGA high-level synthesis
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) provides an easy-to-use abstraction for designing hardware circuits. However, standard datatypes in high-level languages are over provisioned for typical applications, incurring extra area since the underlying FPGA hardware can support arbitrary bitwidths. This area inefficiency can be overcome by enabling the use of arbitrary-width datatypes at the source code level. However, this requires that HLS users spend time and effort on examining all program variables and quantifying their area impact, which can be intractable especially with large, complex programs and time-consuming synthesis. We propose Sensei, an advisor that predicts the post-synthesis area savings brought about by reducing bitwidth and presents users with a ranking of program variables and their area impact. Equipped with a convolutional neural network (CNN)-based predictor, Sensei achieves high area-prediction accuracy and enables rapid exploration of area-saving opportunities.
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