Customised soft processor design: a compromise between architecture description languages and parameterisable processors
Bibliographic record
Abstract
Processor customisation is an effective technique to enhance performance across an application domain. In this study, the authors present a new customised soft processor development environment called polytechnique customised soft processor (PolyCuSP), which bridges the gap between architecture description languages (ADLs) and extensible soft processors. The main objective of this environment is to facilitate rapid design space exploration while preserving a wide range of customisation flexibility. For this purpose, PolyCuSP offers full flexibility in instruction‐set description, while limiting the datapath customisation to a predefined set of tunable microarchitectural parameters. The environment avoids extensive datapath description that is unnecessary for usual microarchitectural customisation techniques in order to simplify the development process. A new XML‐based description format is introduced for instruction‐set modelling. Experimental results evaluate and compare the design and customisation complexities offered by PolyCuSP with competitive approaches. Results demonstrate the efficiency of applying customisation techniques in the proposed environment. For the Sobel edge detection algorithm, the results show that microarchitectural tuning and instruction‐set architecture customisation improve the performance‐per‐cost ratio by an average of 44 and 27%, respectively. Furthermore, in a case study of a tone‐mapping algorithm, PolyCuSP achieves an average improvement of 38% in performance‐per‐cost ratio over an ADL‐based design applying the same customisations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".