Bibliographic record
Abstract
In the presented work the FPGA based run-time reconfigurable platform with temporal partitioning of hardware resources is proposed. This platform is based on the Field Programmable Gate Array (FPGA) device that can be reconfigured "on-fly" to provide the optimal adaptation of a processing architecture to the algorithm and data structure by utilization of developed mechanisms of temporal partitioning of computational / logic resources. It was shown that the proposed approach allows reaching very high cost-effectiveness of the computing platform oriented on processing of framed data-streams. On the other hand, the hardware programming and compilation processes could be simplified by utilization of library of precompiled Virtual Hardware Components stored in the on-board FLASH memory. Paper presents theoretical proof of the proposed approach by analytical comparison of the performance that could be reached on the conventional processors and FPGA platform with Temporal Partitioning Mechanism (TPM) of hardware resources. The implementation of the proposed TPM on the basis of Xilinx Spartan-3 and Xilinx Virtex II FPGA devices is described. Experimental results gained on the prototype of the FPGA based platform with TPM are discussed and analyzed. Keywords: reconfigurable computing, data-stream processing, FPGA, run-time reconfiguration, temporal partitioning.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".