Optimization of HW/SW Co-Design: Relevance to Configurable Processor and FPGA Technology
Why this work is in the frame
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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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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