Optimization of HW/SW Co-Design: Relevance to Configurable Processor and FPGA Technology
Bibliographic record
Abstract
This paper presents a methodology for optimization of HW/SW co-design based on emerging configurable processor and FPGA technologies. This methodology is illustrated by the optimization of a discrete cosine transform (DCT) for image compression based on Tensilica's Xtensa LX core and Xilinx Virtex-II Pro device. The various optimization processes of a 2-D DCT transform, including adding different processor instruction sets onto the base processor to speedup software execution, are described. The results show a 26.76 times speed increase by adding a 4-way SIMD (single instruction multiple data) instruction with moderate hardware cost for a simple 2-D DCT implementation. The optimized 4-way SIMD processor is implemented on the FPGA board to verify the design, and shows a further significant speedup for on-board calculation compared to instruction-set simulation results. The HW vs. SW optimization strategy, speed and HW cost trade-offs, etc. are presented.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".