Custom instruction hardware integration within a SoC hybrid environment
Bibliographic record
Abstract
Traditionally, common processor augmentation solutions have involved the addition of coprocessors or the datapath integration of custom instructions within extensible processors as Instruction Set Extensions (ISE). Rarely is the hybrid option of using both techniques explored. Much research already exists concerning the identification and selection of custom hardware blocks from hardware/software partitioning techniques, but the question of how to best use this hardware within a user system where both coprocessors and datapath augmentations are possible remains. This paper looks to extend existing ISE algorithms which provide custom hardware as dataflow graphs (DFG) and place them appropriately within a hybrid System-on-Chip (SoC) using standard combinatorial optimization techniques. A combinatorial model is presented to address this placement issue and is applied to two well known kernel programs. We further show that such standard techniques can execute within a reasonable time frame alleviating the need for heuristics.
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How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".