Near optimal machine learning based random test generation

The continuing increase in functional requirements of modern hardware designs means the traditional functional verification process becomes inefficient in meeting the time-to-market goal with sufficient level of confidence in the design. Therefore, the need for enhancing the process is evident. Machine learning (ML) models proved to be valuable for automating major parts of the process, which have typically occupied the bandwidth of engineers; diverting them from adding new coverage metrics to make the designs more robust. Current research of deploying different (ML) models prove to be promising in areas such as stimulus constraining, test generation, coverage collection and bug detection and localization. An example of deploying artificial neural network (ANN) in test generation shows 24.5× speed up in functionally verifying a dual-core RISC processor specification. Another study demonstrates how k-means clustering can reduce redundancy of simulation trace dump of an AHB-to-WHISHBONE bridge by 21%, thus reducing the debugging effort by not having to inspect unnecessary waveforms. The surveyed work demonstrates a comprehensive overview of current (ML) models enhancing the functional verification process from which an insight of promising future research areas is inferred.

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Autism Spectrum Disorder Diagnosis using Optimal Machine Learning Methods

International Journal of Advanced Computer Science and Applications ◽

10.14569/ijacsa.2020.0110929 ◽

2020 ◽

Vol 11 (9) ◽

Author(s):

Maitha Rashid Alteneiji ◽

Layla Mohammed ◽

Muhammad Usman

Keyword(s):

Machine Learning ◽

Autism Spectrum Disorder ◽

Autism Spectrum ◽

Spectrum Disorder ◽

Learning Methods ◽

Autism Spectrum Disorder Diagnosis ◽

Machine Learning Methods ◽

Optimal Machine

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Automated Machine Learning on Graphs: A Survey

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/637 ◽

2021 ◽

Author(s):

Ziwei Zhang ◽

Xin Wang ◽

Wenwu Zhu

Keyword(s):

Machine Learning ◽

Learning Algorithm ◽

Research Community ◽

Future Research ◽

Research Directions ◽

Vast Number ◽

Neural Architecture ◽

Future Research Directions ◽

Automated Machine Learning ◽

Optimal Machine

Machine learning on graphs has been extensively studied in both academic and industry. However, as the literature on graph learning booms with a vast number of emerging methods and techniques, it becomes increasingly difficult to manually design the optimal machine learning algorithm for different graph-related tasks. To solve this critical challenge, automated machine learning (AutoML) on graphs which combines the strength of graph machine learning and AutoML together, is gaining attention from the research community. Therefore, we comprehensively survey AutoML on graphs in this paper, primarily focusing on hyper-parameter optimization (HPO) and neural architecture search (NAS) for graph machine learning. We further overview libraries related to automated graph machine learning and in-depth discuss AutoGL, the first dedicated open-source library for AutoML on graphs. In the end, we share our insights on future research directions for automated graph machine learning. This paper is the first systematic and comprehensive review of automated machine learning on graphs to the best of our knowledge.

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