Custom code generation for soft processors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Embedded systems designers that use FPGAs are increasingly including soft processors in 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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