scholarly journals Machine Learning-based Defect Coverage Boosting of Analog Circuits under Measurement Variations

2020 ◽  
Vol 25 (5) ◽  
pp. 1-27
Author(s):  
Nektar Xama ◽  
Martin Andraud ◽  
Jhon Gomez ◽  
Baris Esen ◽  
Wim Dobbelaere ◽  
...  
Keyword(s):  
Machine Learning ◽  
Analog Circuits ◽  
Defect Coverage

Optimization of amplifier circuits by using gradient boosted trees and probability annealing policy

2020 ◽  
Vol 15 (3) ◽  
pp. 1-5
Author(s):  
Evelyn Cristina de Oliveira Lima ◽  
André Borges Cavalcante ◽  
João Viana Da Fonseca Neto
Keyword(s):  
Machine Learning ◽  
Data Collection ◽  
Analog Circuits ◽  
Design Automation ◽  
Optimization Problem ◽  
State Of The Art ◽  
Circuit Parameter ◽  
Learning Module ◽  
Circuit Components ◽  
Parameter Values

One important step of the optimization of analog circuits is to properly size circuit components. Since the quantities that define specification may compete for different circuit parameter values, the optimization of analog circuits befits a hard and costly optimization problem. In this work, we propose two contributions to design automation methodologies based on machine learning. Firstly, we propose a probability annealing policy to boost early data collection and restrict electronic simulations later on in the optimization. Secondly, we employ multiple gradient boosted trees to predict design superiority, which reduces overfitting to learned designs. When compared to the state-of-the art, our approach reduces the number of electronic simulations, the number of queries made to the machine learning module required to finish the optimization.

Mind wandering as data augmentation: How mental travel supports abstraction

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.

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning
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