Bibliographic record
Abstract
Microsoft has clearly made the case for using FPGAs at scale in the cloud and Intel is committed to leveraging the benefits of hardware acceleration with their acquisition of Altera. However, we still cannot use FPGAs with the same ease we have with software-based systems, let alone do it easily at scale in the cloud. High-level synthesis is necessary for making FPGAs accessible, but it is not sufficient. Making FPGAs easy to use for computation requires more than developing accessible tools for creating hardware targeted for FPGAs. The software computing world has a lot of taken-for-granted, sometimes invisible and good open source infrastructure that is missing for using FPGAs as computing devices. The problem is compounded when we want to use FPGAs at the scale of the cloud. I will present the need for some common infrastructure and abstraction layers to support the use of FPGAs for computing at scale, and describe relevant work at the University of Toronto that can contribute towards the development of an open source framework for the use and deployment of FPGAs at scale.
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 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".