Vsd - Machine Intelligence In Eda/Cad

[LeeAndro]

Last updated 4/2019MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 2.57 GB | Duration: 4h 8m

Listen from CEO/architect himself on Machine learning

What you'll 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 pr and introduction to Tensor flow
Applied theory, regression and classification
Requirements
Be familiar to basic VLSI chip design flow
Be familiar with standard nomenclature of VLSI and chip design
Basic knowledge on Python and Tesnsor Flow is nice to have, but will be anyways covered in the course
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
teologies 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.

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Agenda, myths and latest applications of machine intelligence (MI)

Section 2: Intro to Machine Learning in EDA/CAD and frameworks

Lecture 3 MI in design automation and MI categories

Lecture 4 MI architecture and LIVE QnA with participants

Lecture 5 MI foundation and steps to add colaboratory lab for python programming

Lecture 6 Introduction to python scripting

Lecture 7 Quick QnA session with tensor flow

Lecture 8 LIVE QnA with participants regarding tensor flow

Section 3: Wire resistance estimation using regression model

Lecture 9 Regression model, wire resistance estimation and dataset normalization

Lecture 10 ML model, loss function and gradient descent learning algorithm

Lecture 11 LIVE QnA and labs on gradient descent algorithm

Lecture 12 ML solution flow and resistance estimation with linear regression labs

Lecture 13 Training model for resistance estimation with linear regression

Section 4: Error Analysis

Lecture 14 Predicting resistance values and error analysis

Lecture 15 LIVE QnA on regression and resistance estimation

Lecture 16 Wire error model and underfitting concept

Lecture 17 LIVE QnA on wire error model and underfitting

Lecture 18 Million dollar query on parasitics extraction

Section 5: Wire Capacitance Estimation (WiCE)

Lecture 19 Wire capacitance estimation (WiCE), loss function and labs

Lecture 20 WiCE labs and exercise description

Lecture 21 LIVE QnA with participants on WiCE

Section 6: Cell classification

Lecture 22 Classification examples, algorithms and decision boundary

Lecture 23 VLSI cell classification (VCC) and data-set

Lecture 24 Logistic regression, VCC machine learning model and VCC loss function

Lecture 25 Labs on binary classification of cells using logistic regression

Lecture 26 Confusion matrix

Section 7: Conclusion

Lecture 27 Support vector machine algorithm and conclusion

Design automation eeers,CAD developers,Managers and executives,Research professionals and graduate students,Machine learning enthusiasts and Investors

HomePage:

Https://anonymz.com/https://www.udemy.com/course/vsd-machine-intelligence-in-eda-cad/

DOWNLOAD

https://1dl.net/w1wfkgdfsylr/NPfvzaCL__VSD_Machin.part1.rar.html
https://1dl.net/43mei2s92sqy/NPfvzaCL__VSD_Machin.part2.rar.html
https://1dl.net/zhl1iewvi7rd/NPfvzaCL__VSD_Machin.part3.rar.html