Application Specific Instruction-Set Processor Generation for Video Processing Based on Loop Optimization
Why this work is in the frame
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Bibliographic record
Abstract
Until recently, application specific instruction-set processor (ASIP) design was very costly and complex. Now, ASIP circuits are much easier to develop with technologies like Tensilica and Altera configurable processors that provide tools enabling effective generation of RTL (register transfer level) code for ASIPs. On the other hand, the design of effective ASIPs is still time-consuming, because existing methodologies largely rely on designers' knowledge for design space exploration. The paper describes a methodology to help design ASIPs. An iterative profiling-driven method based on detection and acceleration of application bottlenecks with specialized instructions is proposed. This method is applied to the design of an ASIP adapted for a video processing algorithm - the Wiener filter. The acceleration reached with our method on this application is very significant, with a speedup factor larger than 10 over optimized software code.
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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 | 0.001 |
| 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