Special Session: Machine Learning for Semiconductor Test and Reliability

Abstract The increasing volume of biomedical data in chemistry and life sciences requires development of new methods and approaches for their analysis. Artificial Intelligence and machine learning, especially neural networks, are increasingly used in the chemical industry, in particular with respect to Big Data. This editorial highlights the main results presented during the special session of the International Conference on Neural Networks organized by “Big Data in Chemistry” project and draws perspectives on the future progress of the field. Graphical Abstract

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Online and Offline Machine Learning for Industrial Design Flow Tuning: (Invited - ICCAD Special Session Paper)

10.1109/iccad51958.2021.9643577 ◽

2021 ◽

Author(s):

Matthew M. Ziegler ◽

Jihye Kwon ◽

Hung-Yi Liu ◽

Luca P. Carloni

Keyword(s):

Machine Learning ◽

Industrial Design ◽

Design Flow ◽

Special Session ◽

Session Paper

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Special session: SoC architectures for machine learning through inexactness

2016 29th IEEE International System-on-Chip Conference (SOCC) ◽

10.1109/socc.2016.7905475 ◽

2016 ◽

Author(s):

Seongsoo Hong ◽

Anshumali Shrivastava

Keyword(s):

Machine Learning ◽

Special Session

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T1B: Special session: Data analytics driven design for yield, manufacturability and reliability: Where machine learning meets design automation

2017 30th IEEE International System-on-Chip Conference (SOCC) ◽

10.1109/socc.2017.8226045 ◽

2017 ◽

Author(s):

Johanna Sepulveda

Keyword(s):

Machine Learning ◽

Design Automation ◽

Data Analytics ◽

Special Session ◽

Design For Yield

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Special Session – Machine Learning in Test: A Survey of Analog, Digital, Memory, and RF Integrated Circuits

2021 IEEE 39th VLSI Test Symposium (VTS) ◽

10.1109/vts50974.2021.9441051 ◽

2021 ◽

Author(s):

Soham Roy ◽

Spencer K. Millican ◽

Vishwani D. Agrawal

Keyword(s):

Machine Learning ◽

Integrated Circuits ◽

Special Session ◽

Rf Integrated Circuits ◽

Digital Memory

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Mind wandering as data augmentation: How mental travel supports abstraction

Behavioral and Brain Sciences ◽

10.1017/s0140525x1900311x ◽

2020 ◽

Vol 43 ◽

Author(s):

Myrthe Faber

Keyword(s):

Machine Learning ◽

Data Augmentation ◽

Mental Content ◽

Mind Wandering ◽

Theoretical Framework ◽

Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

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