Custom code generation for soft processors
Bibliographic record
Abstract
Embedded systems designers that use FPGAs are increasingly includingsoft processorsin their designs (configurable processors built in the programmable logic of the FPGA). While there has been a significant amount of research on adding custom instructions and accelerators to soft processors, these are typically used to extend an unmodified base ISA targeted by generic compilation such as with unmodified gcc. In this paper we explore several opportunities for the compiler to optimize the code generated for soft processors through application-specific customization of the base ISA---techniques that are orthogonal to adding custom instructions. In particular we explore: (i) low level software-hardware trade-offs between basic instructions; (ii) the utility of ISA-specific features---in particular for the delay slots and Hi/Lo registers in the MIPS ISA; and (iii) application specific register management. We find that through these techniques that have no hardware cost we can improve the area efficiency of soft processors by 12% on average across a suite of benchmarks, and by up to 47% in the best case.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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) | 0.009 | 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".