Performance-optimized FPGA implementation for the flexible triangle search block-based motion estimation algorithm
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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%.
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.
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 | 0.001 | 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) | 0.000 | 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