Proceedings of the 23rd symposium on Integrated circuits and system design
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 23rd Symposium on Integrated Circuits and Systems Design -- SBCCI'10. The mission of the symposium is to share ideas and to point to new directions for future research and development. SBCCI offers researchers and practitioners a unique opportunity to share their perspectives with those interested in the various aspects of microelectronics. This year's symposium continues its tradition of being a premier forum for the presentation of leading edge research on integrated circuit and systems, design methodologies and CAD algorithms. SBCCI'10 call for papers attracted 101 submissions comprising 19 different countries from Africa, Asia, Canada, Europe, South America and the United States. The program committee accepted 37 papers that cover a variety of topics, ranging from novel RF circuit and low-power techniques, to advancements in CAD and SoC design. In addition, the program includes three envisioning talks from leading academia and industry researchers, which cover three major issues in the design of nanoscale circuits and systems: power consumption, design reliability and testing. We hope that these proceedings will serve as a valuable reference for researchers and developers alike.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.040 |
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".