Bibliographic record
Abstract
Many of us discovered that we were working in System-on-Chip technology by default! The technology to put hundreds of millions of transistors on a monolithic CMOS digital chip became available over the past few years; we adopted it and became de facto SoC researchers. However, as SoC has come on stream we have also started to see cracks appearing in the technology. 3rd order effects ofjust a few years ago have become predominant problems, and previous performance predictions have been shown to be false. This talk will undoubtedly produce more questions than answers, but, as an interested observer of the technologies we play with in our sand box, I will try to ponder on some of the issues - and muse on how those amongst us, who normally only observe, can also play a role in defining and exploring promising future technologies. Brief Bio: Graham Jullien holds the iCORE Chair in Advanced Technology Information Processing Systems, and is the Director of the ATIPS Laboratories, in the Department of Electrical and Computer Engineering at the University of Calgary. His long-term research interests are in the areas of Integrated Circuits (including SoC), VLSI Signal Processing, Computer Arithmetic, High Performance Parallel Architectures, and Number Theoretic Techniques. Since taking up his chair position at Calgary in 2001, he has expanded his research interests to include security systems, nano-electronic technologies and biomedical systems. He is currently involved, along with his colleagues, in developing an Integration Laboratory cluster to explore next generation integrated microsystems. Dr.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.021 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.027 | 0.010 |
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".