Performance-optimized FPGA implementation for the flexible triangle search block-based motion estimation algorithm
Bibliographic record
Abstract
This paper presents a performance-optimized version of the flexible triangle (FTS) block-matching search algorithm. The FTS is a fast block-matching algorithm for motion estimation proposed in previous work that, given a block of pixels, is used to search for the best-matching block in a given search area using only a selected subset of available positions rather than searching all available positions as done by full search algorithm which is computationally very expensive. Further analysis to previous FPGA implementation of the FTS indicates that additional parallelism can be employed to improve the overall processing time of the FTS algorithm. In addition to this, investigating the performance bottlenecks and redesigning some of the used hardware modules can increase the maximum supported frequency for the entire FTS FPGA implementation. The proposed design changes were implemented in VHDL and synthesized for using Xilinx virtex-5. Simulation results indicate that the proposed implementation reduced the average number of cycles required to process a block by 17%. Moreover, synthesis results indicate that the proposed design is able to increase the maximum supported frequency by around 38% compared to the previous FPGA implementation of the FTS algorithm. Consequently, the maximum supported frame rate has been increased by around 66%.
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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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".