Implementation and trade-offs of a DCT architecture using high-level synthesis
Why this work is in the frame
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Bibliographic record
Abstract
This paper presents architectural trade-offs of a time-shared implementation of a modified fast discrete cosine transform algorithm using a high-level synthesis tool. The architecture presented here allows time-sharing of operators in different stages. The overhead in control and multiplexing is minimal. A full implementation of an 8/spl times/8 2-D DCT outperforms the original pipelined architecture and a hand-crafted time-shared architecture by reducing the required area by up to 50%. It also improves the latency by up to 70%. It achieves these improvements maintaining the throughput for a 5% decrease in the required critical path timing. The complexity of the 2-D DCT used is higher than the traditional benchmarks for high-level synthesis. This paper shows the effectiveness of the synthesis tool used for large, practical algorithms.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it