VSD - Machine Intelligence in EDA/CAD
4.2 (35 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
220 students enrolled

VSD - Machine Intelligence in EDA/CAD

Listen from CEO/architect himself on Machine learning
4.2 (35 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
220 students enrolled
Last updated 4/2018
English
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This course includes
  • 4 hours on-demand video
  • 2 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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 primer 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 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.



Who this course is for:
  • Design automation engineers
  • CAD developers
  • Managers and executives
  • Research professionals and graduate students
  • Machine learning enthusiasts and Investors
Course content
Expand all 27 lectures 04:08:00
+ Introduction
2 lectures 20:05
Agenda, myths and latest applications of machine intelligence (MI)
09:41
+ Intro to Machine Learning in EDA/CAD and frameworks
6 lectures 56:25
MI in design automation and MI categories
09:09
MI architecture and LIVE QnA with participants
12:23
MI foundation and steps to add colaboratory lab for python programming
09:00
Quick QnA session with tensor flow
09:59
LIVE QnA with participants regarding tensor flow
05:23
+ Wire resistance estimation using regression model
5 lectures 51:38
Regression model, wire resistance estimation and dataset normalization
09:13
ML model, loss function and gradient descent learning algorithm
10:36
LIVE QnA and labs on gradient descent algorithm
11:31
Training model for resistance estimation with linear regression
10:25
+ Error Analysis
5 lectures 47:55
Predicting resistance values and error analysis
09:22
LIVE QnA on regression and resistance estimation
09:22
Wire error model and underfitting concept
10:46
LIVE QnA on wire error model and underfitting
11:00
+ Wire Capacitance Estimation (WiCE)
3 lectures 21:19
Wire capacitance estimation (WiCE), loss function and labs
09:28
LIVE QnA with participants on WiCE
03:43
+ Cell classification
5 lectures 41:44
Classification examples, algorithms and decision boundary
11:10
VLSI cell classification (VCC) and data-set
08:00
Logistic regression, VCC machine learning model and VCC loss function
10:04
Confusion matrix
04:26