Proceedings of the 12th ACM Great Lakes symposium on VLSI
Bibliographic record
Abstract
Welcome to the Big Apple for the 12-- th Great Lakes VLSI Symposium. While New York City is not exactly on the Great Lakes, it does have a connection to Lake Ontario through the Hudson and the old waterways. This justifies the choice of the Big Apple as the venue for this year's GLSVLSI! Once again this symposium has attracted an excellent assortment of papers, over a range of topics that are fundamental to advancing the state of the art.This years program has been carefully selected by the program co-- chairs through peer review, with each paper getting at least three reviews, in a record review period of just one month. Our kudos go to the program committee members and additional reviewers for completing their hard work in such a short time. We believe that we have a strong and interesting selection of papers for this symposium covering all major aspects of VLSI design.This year, we received 71 uniformly high quality submissions. We would like to thank all the authors who submitted their manuscripts for consideration. The technical program committee had great difficulty in limiting the number of accepted papers to fit time constraints of the conference. Of the submitted papers, 19 were accepted as full papers, 12 as short papers, and an additional 10 as posters. Approximately half of the full and short papers cover some aspect of VLSI CAD. A third discuss circuit related topics, and another third cover specific chip, subsystem, or systemdesign. A tenth address topics of a more theoretical nature, while a full 25% touch on the increasingly important area of low power. From our perspective, this is a very satisfying mix.
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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.194 | 0.127 |
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".