Proceedings of the 2007 international workshop on System level interconnect prediction
Bibliographic record
Abstract
On behalf of the organizing committee, we would like to welcome you to the 9th International Workshop on System-Level Interconnect Prediction (SLIP'07), held on March 17-18, 2007 at the Dolce Lakeway Resort and Spa in Austin, Texas. The SLIP workshop focuses on modeling and prediction of usable properties of optimized interconnect systems and their impact on system performance. Both theory and applications of interconnect prediction techniques are highlighted, with emphasis on applications to architectural and micro-architectural exploration, physical design, interconnect technology planning, and communication networks. In addition to the presentation of state-of-the-art papers in these fields, invited talks and tutorials by leading researchers aim to encourage dialogue between the architecture, physical design, and interconnect technology communities. Following a rigorous review process, the program committee has selected an outstanding set of 12 papers for publication in the proceedings and oral presentations at the workshop. The topics of selected papers cover a wide range of issues, including impact of new materials (e.g., low-k dielectrics, carbon nanotube bundles) on interconnect performance, congestion estimation and early interconnect characterization and planning, and the impact of process variation on interconnect performance. In addition to the contributed papers, we continue the SLIP tradition of featuring in the technical program several distinguished invited speakers. This year Avinoam Kolodny from Technion will speak about networks on chips, Majid Sarrafzadeh from UCLA will give a tutorial on congestion prediction, and Charles Alpert from IBM Austin Research Laboratory will speak on physical synthesis.
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 | 0.002 | 0.004 |
| 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 | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.019 |
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".