VSD - Machine Intelligence in EDA/CAD

Listen from CEO/architect himself on Machine learning

4.45 (212 reviews)
Udemy
platform
English
language
Data Science
category
instructor
814
students
4 hours
content
Apr 2019
last update
$34.99
regular price

What you will learn

Intro to Machine Learning in EDA/CAD

Develop machine learning apps with TensorfFow and Python in cloud

Develop EDA and CAD applications like resistance estimation, capacitance estimation, cell classification etc.

Categories of Machine Learning

Machine Learning Framework which will cover Python primer and introduction to Tensor flow

Applied theory, regression and classification

Description

This webinar was conducted on 31st March 2018 with Rohit, CEO Paripath Inc.

We start with Electronic design automation and what is machine learning. Then we will give overall introduction to categories of machine learning (supervised and unsupervised learning) and go about discussing that a little bit. Then we talk about the frameworks which are available today, like general purpose, big data processing and deep-learning, and which one is suitable for design automation. This is Machine Learning in general with a focus on CAD, EDA and VLSI flows.

Then we talk about Applied Theory (data sets, data analysis like data augmentation, exploratory data analysis, normalization, randomization), as to what are the terms and terminologies and what do we do with that, accuracy, how do we develop the algorithm, essentially the things that are required to develop the solution flow, lets say, you as the company wants to add a feature in your product using machine learning, what you would be doing, and what your flow will look like and this is what is shown as pre-cursor of flight theory as what you should be looking out.

And then we start with regression, which is first in supervised learning. In the regression, we will give couple of example, like first is resistance estimation, second is polynomial regression which is capacitance estimation. For resistance estimation, we have the dataset from 20nm technology. And finally, we go on to create a linear classifier using logistic regression.

Next will be dimensionality reduction, meaning, you have a large dataset and how to you reduce the size of that so that you can run on a laptop or even on your cell phone. Then there is a big example of that. Everything has mathematics behind that, this wont be a part of the webinar.


About Rohit - Rohit Sharma is Founder and CEO of Paripath Inc based in Milpitas, CA. He graduated from IIT Delhi.He has authored 2 books and published several papers in international conferences and journals. He has contributed to electronic design automation domain for over 20 years learning, improvising and designing solutions. He is passionate about many technical topics including Machine Learning, Analysis, Characterization and Modeling, which led him to architect guna - an advanced characterization software for modern nodes.He currently works for Paripath Inc.



Content

Introduction

Introduction
Agenda, myths and latest applications of machine intelligence (MI)

Intro to Machine Learning in EDA/CAD and frameworks

MI in design automation and MI categories
MI architecture and LIVE QnA with participants
MI foundation and steps to add colaboratory lab for python programming
Introduction to python scripting
Quick QnA session with tensor flow
LIVE QnA with participants regarding tensor flow

Wire resistance estimation using regression model

Regression model, wire resistance estimation and dataset normalization
ML model, loss function and gradient descent learning algorithm
LIVE QnA and labs on gradient descent algorithm
ML solution flow and resistance estimation with linear regression labs
Training model for resistance estimation with linear regression

Error Analysis

Predicting resistance values and error analysis
LIVE QnA on regression and resistance estimation
Wire error model and underfitting concept
LIVE QnA on wire error model and underfitting
Million dollar query on parasitics extraction

Wire Capacitance Estimation (WiCE)

Wire capacitance estimation (WiCE), loss function and labs
WiCE labs and exercise description
LIVE QnA with participants on WiCE

Cell classification

Classification examples, algorithms and decision boundary
VLSI cell classification (VCC) and data-set
Logistic regression, VCC machine learning model and VCC loss function
Labs on binary classification of cells using logistic regression
Confusion matrix

Conclusion

Support vector machine algorithm and conclusion

Screenshots

VSD - Machine Intelligence in EDA/CAD - Screenshot_01VSD - Machine Intelligence in EDA/CAD - Screenshot_02VSD - Machine Intelligence in EDA/CAD - Screenshot_03VSD - Machine Intelligence in EDA/CAD - Screenshot_04

Reviews

Rajesh
April 13, 2020
Very Interesting! Although this domain is completely new for me but got the crux of many concepts and beliving as things will be clear by practising the labs once again.
RATHEESHWARAA
August 18, 2019
The course was developed well by instructor with good presentation. But the explanation for some questions are quite hard to understand.
Ajinkya
July 31, 2019
Yes,it was good to learn this course.But,it could have been better if they had provided any example of machine learning in VLSI CHIP designing steps.ex:timing,placement or routing optimization
Praveen
July 27, 2019
Good course but little fast-paced and requires background knowledge on Python as well as programming concepts.
Parakh
July 26, 2019
Amazing!!! Genuinely, if you are good on the basics of ML, till validating a model... take on this course! Thanks for such a course.
Ranganayakulu
June 21, 2018
Rohit has explained a lot of concepts in a simplified manner in this short ~3-hour webinar. It serves as a beginner guide to starting the journey into machine learning and deep learning topics. I definitely recommend this course to all those who are interested in this topic.
Ravit
April 11, 2018
Extensive content on machine learning, infrastructure, applied theory, programming and application. Complete coverage with swift pace.
Yogesh
April 9, 2018
Overall this is good and course content is promising, very useful. The issue I have is with the pace (specifically few things that I think from ML perspective are very interesting). I think at places pace is faster (specifically when it gets into building the model). People who already have some idea may be OK but for others it is difficult to keep pace. I think the need to finish it in 4 hours is the cause but if understanding is not complete then rest of it doesn't help.
Tanmay
April 5, 2018
Thank you for the course.. Nice explanation. I learned more by watching these videos rather than other online tutorials. I will follow the other courses too.

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1626838
udemy ID
4/2/2018
course created date
11/24/2019
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