Fast Logic Optimization Using Decision Trees

Abreu, Brunno; Berndt, Augusto; Campos, Isac S.; Meinhardt, Cristina; Carvalho, Jônata Tyska; Grellert, Mateus; Bampi, Sérgio

doi:10.1109/iscas51556.2021.9401664

Cited by 7 publications

(2 citation statements)

References 16 publications

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“…This is one of the supervised ML models used for both classification and regression [33]. Its structure resembles a flowchart and is made up of internal nodes, branches, and leaf nodes.…”

Section: ) Dtmentioning

confidence: 99%

Dimensionality Reduction by Machine Learning for Cost-Effective Data Analysis

Asaduzzaman,

Uddin,

Sibai

2024

Preprint

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Processing large amount of data with many input features is always time consuming and expensive. In machine learning (ML), the number of input features play a crucial role in determining the performance of the ML models. Studies show that ML has potential for dimensionality reduction. This work proposes a methodology to reduce the number of input features using ML to facilitate cost-effective data analysis. Two different data sets for water quality prediction from Kaggle are used to run the ML models. First, we use Recursive Feature Elimination with Cross-Validation (RFECV), Permutation Importance (PI), and Random Forest (RF) models to find the impact of input features on predicting water quality. Second, we conduct experiments applying seven ML models: RF, Decision Tree (DT), Logistic Regression (LR), K-Nearest Neighbors (KNN), Gaussian Naïve Bayes (GNB), Support Vector Machine (SVM), and Deep Neural Network (DNN) to explore water quality using the original and reduced datasets. Third, we evaluate the impact of the optimized data features on computations and cost to test water quality. Experimental results show that reducing the number of features from nine to five for Dataset 1 helps reduce computations by up to 59% and cost up to 65%. Similarly, reducing the number of features from 20 to 16 for Dataset 2 helps reduce computations by up to 20% and cost up to 14%. This study may help mitigate the curse of dimensionality, via improving the performance of ML models by enhancing data generalization.

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“…This is one of the supervised ML models used for both classification and regression [33]. Its structure resembles a flowchart and is made up of internal nodes, branches, and leaf nodes.…”

Section: ) Dtmentioning

confidence: 99%

Dimensionality Reduction by Machine Learning for Cost-Effective Data Analysis

Asaduzzaman,

Uddin,

Sibai

2024

Preprint

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“…They usually represent the logic structure itself, meaning a direct mapping is realized from DT or RF structure to the Boolean network. In Abreu [48], a logic optimization technique focused on fast synthesis is proposed using DT, presenting superior results in runtime compared to ABC and Espresso techniques with total accuracy. This approach explores the creation of DT trained on top of a given truth table.…”

Section: B Logic Learningmentioning

confidence: 99%

Review of Machine Learning in Logic Synthesis

Berndt

Fogaça

Meinhardt

2022

JICS

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Electronic design automation tools have multiple options that need to be tuned for specific designs and technology nodes. Traditionally, the tuning process is done by teams of expert engineers and demands a large amount of computational resources.In recent years, there has been an increased effort to apply machine learning techniques in electronic design automation problems, attempting to increase the design flow correlation and predictability, hence reducing the time spent on tuning.In this work, we revise modern approaches in electronic design automation and machine learning techniques applied during logic synthesis. We categorize and discuss their core technologies, such as transforming data into images. Machine learning techniques are as good as the available data. Thus, we present existing learning datasets for logic synthesis and strategies such as data augmentation to overcome the lack of specific data for logic synthesis problems.To cope with these problems, we discuss how research is shifting from traditional supervised learning techniques to reinforcement learning-based methods.

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