Designing customized microprocessors for fixed-point computation
Bibliographic record
Abstract
This paper proposes a method to optimize application-specific microprocessors for fixed-point computations. Fixed-point word-length optimization is a well-known research area that aims to find the optimal trade-offs between accuracy and hardware cost in bitwidth allocation signals in fixed point circuits. This work proposes a methodology to combine word-length optimization with application-specific processor customization. The goal is to optimize the following parameters in the processor architecture: (1) datatype word-lengths, (2) size of register-files and (3) architecture of the functional units. Multi-level evolutionary algorithms are employed to perform the optimization. To facilitate evaluation, a new processor design environment was developed that supports necessary customization flexibility to realize and evaluate the proposed methodology. The experimental results show that for five evaluated benchmarks, the proposed methodology can reduce the number of consumed LUTs and flip-flops by an average of 11.9% and 5.1%, respectively, while reducing the latency by an average of 33.4%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".