Evolvable hardware design based on a novel simulated annealing in an embedded system
Bibliographic record
Abstract
SUMMARY The auto‐design of electronic circuits for the next generation Information Technology (IT) computing environments is currently one of the most extensively studied issues in the field of evolvable hardware (EHW) architectures. It aims to improve the reliability and fault‐tolerance of hardware systems using embedded techniques. As the scalability of logic circuits becomes larger and more complex nowadays, its auto‐design is more and more difficult. In order to improve the efficiency and the capability of digital circuit auto‐design, in this paper, a multi‐objective simulated annealing (MSA)‐based increasable evolution approach is proposed in an embedded system. First, an extended matrix encoding method is used to indicate the potential performance of a circuit. Therefore, the risk of deleting a circuit with a good developing potential during evolution can be reduced. Second, we consider each output of a digital circuit as an objective, and MSA is designed for digital logic circuits with gradual evolution scheme. In the process of evolution, each objective is evolved in parallel with adaptive mechanism of neighborhood and a performance evaluation. Finally, a framework of online evolution with macro‐blocks is employed to implement MSA on a field‐programmable gate array efficiently and securely. In our experiments, six arithmetic circuits are designed to assess the performance of MSA with gate‐level and function‐level approaches comparing to other algorithms. The comparison results show that our method is very efficient in the auto‐design of EHW. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.000 |
| 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 | 0.000 |
| 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".