Bibliographic record
Abstract
In our work with developing an OpenCL platform for FPGAs, we observed that the way that OpenCL is currently used on FPGAs does not expose the full capability of FPGAs to the programmer. In particular, FPGAs are spatial devices that can be partitioned by area with each partition programmed with a different function. The latest FPGAs can even be reconfigured dynamically such that one partition of the FPGA can be configured while the rest of the FPGA is still in use. The analogy with GPUs is that an OpenCL programmer can partition a GPU into multiple device objects, execute different kernels on each device object, and reprogram the device objects. An OpenCL programmer cannot do this with an FPGA even though the capability exists. As FPGA capacities continue to increase, the ability to partition and partially reconfigure the FPGA will become even more desirable. The fundamental issue is how FPGAs are currently viewed as devices in the OpenCL model. In this paper, we propose a small change to the OpenCL definition of a device that unlocks the full potential of FPGAs to the programmer.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".