Anomaly Detection in Dynamic Power Events Using Data Fusion for Chip Design
Bibliographic record
Abstract
This paper describes a methodology to detect anomalies in estimated dynamic power series reported by elec-tronic design automation (EDA) tools during very-large-scale integration (VLSI) semiconductor chip design. EDA software is commonly used to predict the expected behaviour of a chip prior to its manufacturing. However, the extremely complex nature of modern chip design and various simulation environment circum-stances may lead to false fluctuation (anomalies) in reported dynamic power series values, resulting in the over-allocation of resources to handle these suspected power variations. The challenge in identifying these anomalies arises from numerous testbench and design particularities, clock model jitters and a lack of an established automated process of incorporating prior data into the decision process. Given that the power fluctuation on the chip is attributed to design activities (data burst, activation of clocks, among many others), an anomaly identification approach utilizing a combination of machine learning and data fusion is proposed and compared to several other approaches. The performance of the proposed methods is evaluated using a simulated dataset consisting of different power patterns exercised over system-on-chips (SoC) design and reported with the EDA tool, post-processed by our suggested algorithm.
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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 | 0.001 | 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".