Design Space Exploration using Parameterized Cores: A Case Study
Bibliographic record
Abstract
Today, many designers of embedded systems are choosing to build their systems using parameterized intellectual property (IP) cores, which are hardware or software components which allow certain aspects of their architecture to be changed and set at design-time. Design space exploration (DSE) is the process of determining the best combination of parameter values from the complete set of possibile designs. Designs are evaluated in terms of their objectives-usually IC chip area, power consumption and system performance. Often, automated approaches are used to prune the design space in search of the Pareto-optimal set of designs. One of the most common approaches involves using a genetic-algorithm (GA) based approach to determine this set from the complete design space. In this paper, we present the results of a case study involving the Altera Nios parameterized soft-core processor. The goal of this study is to determine the Pareto-optimal set of design configurations for the Nios processor using a genetic-based approach-the Simple Evolutionary Algorithm for Multi-objective Optimization (SEAMO). From this study we conclude that genetic-based approaches can be useful in assisting designers to make intelligent choices for parameter selection.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".