Accuracy‐aware processor customisation for fixed‐point arithmetic
Bibliographic record
Abstract
Application‐specific customisation of micro‐processor architectures has been widely accepted as an effective way to improve the efficiency of processor‐based designs. In this work, the authors propose a new processor customisation method based on fixed‐point word‐length optimisation. Accuracy‐aware word‐length optimisation (WLO) of fixed‐point circuits is an active research area with a large body of literature. For the first time, this work introduces a method to combine the WLO with the processor customisation. The data type word‐lengths, the size of register‐files and the architecture of the functional units are the main target objectives to be optimised. Accuracy requirements, defined as the worst‐case error bound, is the key consideration that must be met by any solution. A custom processor design environment, called PolyCuSP, is used to realise the processor architecture based on the solution found in the proposed optimisation algorithm. The results achieved by evaluating five benchmark show that this method can reduce the number of necessary LUTs and flip‐flops by an average of 11.9% and 5.1%, respectively. The latency is also improved by an average of 33.4%. Moreover, the method was further examined through a case study on a JPEG decoder. The results suggest 16.2% and 56.2% reduction in area consumption and latency, respectively.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".