A novel framework of Optimizing modular computing architecture for multi objective VLSI designs
Bibliographic record
Abstract
For the past few years modular design has become the de facto standard for the development of complex VLSI systems. Most of these modular VLSI system designs are generally multi objective in nature with the requisite to tradeoff between many contradictory parameters like speed, power consumed, cost and hardware area. They are heavily used in low end ASIC's which demand low power consumption and cost with acceptable performance and in high end ASIC's with high performance requirement. This paper presents a novel framework for the optimization of computing architecture based on hierarchy factor method. The determination of this hierarchy factor enables the designer to arrange the various resources of the system in the form of an architecture tree based on the application and the user specifications. The resulting structure would act as a pathway for obtaining the optimal architecture design option for multi objective optimization of the computing architecture used in many VLSI designs. The framework for optimization of computing architecture shown in this paper has been deduced and proved mathematically. The proposed method is capable to determine the most influential resource for a certain performance parameter in the whole system which is deduced by considering the mathematical model of the performance metric. The representation of our approach in the form of architecture tree allows easy automation of the process, useful for many multi objective optimized VLSI designs.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".