Automated Generation of Custom Processor Core from C Code
Bibliographic record
Abstract
We present a method for construction of application‐specific processor cores from a given C code. Our approach consists of three phases. We start by quantifying the properties of the C code in terms of operation types, available parallelism, and other metrics. We then create an initial data path to exploit the available parallelism. We then apply designer‐guided constraints to an interactive data path refinement algorithm that attempts to reduce the number of the most expensive components while meeting the constraints. Our experimental results show that our technique scales very well with the size of the C code. We demonstrate the efficiency of our technique on wide range of applications, from standard academic benchmarks to industrial size examples like the MP3 decoder. Each processor core was constructed and refined in under a minute, allowing the designer to explore several different configurations in much less time than needed for manual design. We compared our selection algorithm to the manual selection in terms of cost/performance and showed that our optimization technique achieves better cost/performance trade‐off. We also synthesized our designs with programmable controller and, on average, the refined core have only 23% latency overhead, twice as many block RAMs and 36% fewer slices compared to the respective manual designs.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".