Power Efficient Gurumukhi Unicode Reader Design and Implementation on FPGA
Bibliographic record
Abstract
Gurumukhi is found to be the most widely used language of Pakistan, and it is ranked 3rd in Canada, 7th in India and almost 4th most spoken language in U.K. This Unicode Reader is cost effective solution for learning as well as understanding the Punjabi language by the people across the globe .This reader helps the user to understand, whether written text is consonants, vowels or digits of Gurumukhi scripts. This paper can also be the solution to the various problems occurred in research of Punjabi natural language processing. Hardware is designed for Gurumukhi Unicode Reader (GUR) and is implemented on Virtex-6 FPGA on Xilinx software. This GUR design is tested on different frequencies by applying frequency scaling techniques .The reader is also observed on different IO Standards of two logic families i.e. on SSTL (Stub-Series Terminated Logic) and LVDCI (Low Voltage Digitally Controlled Impedance) logic families to make this design more energy efficient. It is concluded that using LVDCI_DV2_15 rather than SSTL18_II_DCI, the total power can be saved up to 51.22% with the device operating at a frequency of 1MHz.
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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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".