Machine Intelligence for Design Automation

Sep 14, 2018Download as pptx, pdf0 likes214 views

The document discusses opportunities for using machine intelligence (MI) and machine learning (ML) in electronic design automation (EDA). It notes that MI could improve usability, accuracy, and performance of EDA tools through features rather than new products. The Paripath MI platform is introduced for developing MI applications for EDA using technologies like TensorFlow. An example application of using ML to classify VLSI cell types from extracted netlists is presented. Challenges and potential applications of MI in EDA are also discussed.

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
Electronic Design Process Symposium 2018
Rohit Sharma
Machine Intelligence for
Design Automation

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
MIDA: Opportunities
MI and EDA are both growing
 AI/ML to add $15.7 trillion in global economy by 2030. [Reference: PwC report]
 Design Automation Market to hit $14 billion by 2024. [Reference: GMI report]

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
MIDA: Opportunities
AI/ML research rate is phenomenal
 Papers: well over 100k per year. [Reference: ScienceDirect APIs]
 Funding: AI/ML research is over 1% of total global research.

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
MIDA: Opportunities
 The change will likely come in the form of product
features, not new products or flows.
 MI features will bring better usability, accuracy and
performance.
 MI won’t bring a disruptive change to EDA unlike other
industries.
Yes, EDA is ready to make use of MI research

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
Paripath MI Platform
Mathematics
Algebra Calculus Statistics Probability
CAD Infrastructure
Python
TensorFlow Numpy Pyplot
TCL
CLI
C++
EDA
Algorithms
Jupyter
Google
Colab
… Proprietary
Guna PASER
Community
apps
Third party
apps

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
VLSI Cell Classification
Train a machine learning model to classify the type of IP with a fully extracted netlist.
Char Case Study: VCC Classification
IP
Classifier
Graph
Matrix
Circuit
graph

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
 VCC is a common design
automation problem used for
detecting patterns in ASIC, AMS and
Custom flows.
 It is a pattern detection algorithm in
its generalized form and applicable
to many other problems including
DRC, Extraction, Circuit Simulation
and others.
VCC Applications

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
VCC Dimensionality
Press F5 to
start animation

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
GUNA: MI enabled Char Platform
❏ Guna is built on Paripath’s MI platform to offer machine
learning in the cloud with VCC and other MI features.
❏ VCC repository dramatically improves ease of on pre-
characterized cells.
❏ Helps with setup for re-characterization, multiple corners new process
nodes.
❏ VCC feature adds to QA confidence of models.

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
MIDA: Challenges
Clear Value Proposition
Use Model
Data Engineering
High Dimensionality
MI Technology Selection
Integration into Legacy Systems
Acceptance of Probabilistic Results
Design
Automation

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
MIDA: Applications
 Capacitance estimation
 Wire load Models estimation
 RC-tree estimation
 Early timing analysis
 RTL power analysis
 Cell and IP classification
 Verification Coverage
 Fast extraction
 Speed up circuit simulation
 Design Segmentation

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
 Guna is a ML enabled characterization platform that
integrates and adds value to simulation environments.
 Paripath offers community MIDA platform to quickly
customize several well known MI techniques in design
automation.
 MI will likely change the way EDA software is written.
MIDA: Summary & QA

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
QA session

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
 The VCC dataset consists of over
12,000 cells from multiple standard
cell libraries with a variety of
representative cells.
 RC data was stripped from fully
extracted cell netlist and resulting
netlist was represented as
numbers.
 Cells were given 10 different
labels.
VCC Dataset

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
MIDA: Solution Flow
Data
Acquisition
Data
Analysis
Model
Hypothesis
Training Validation Deployment

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
Case Study: Ensemble
K-nearest neighbors
Bayesian networks
Decision trees
Boosted trees
Logistic Regression
Support vector machines
Neural networks
Random forests

© 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com
VCC Design Results
…
…
…
…
…
…
…
…

Motivation behind this talk is to throw some light on use of machine intelligence in design automation; a topic that is largely absent from the media and academia. Machine Intelligence is advancing at a rapid pace and claim to this fame is that it is bound to enable an unprecedent degree of automation in every walk of life. Design automation, a field that has been automating semiconductor design for decades, continues to struggle successful applications of Machine Learning.

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Machine Intelligence for Design Automation

2. © 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com MIDA: Opportunities MI and EDA are both growing  AI/ML to add $15.7 trillion in global economy by 2030. [Reference: PwC report]  Design Automation Market to hit $14 billion by 2024. [Reference: GMI report]

3. © 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com MIDA: Opportunities AI/ML research rate is phenomenal  Papers: well over 100k per year. [Reference: ScienceDirect APIs]  Funding: AI/ML research is over 1% of total global research.

4. © 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com MIDA: Opportunities  The change will likely come in the form of product features, not new products or flows.  MI features will bring better usability, accuracy and performance.  MI won’t bring a disruptive change to EDA unlike other industries. Yes, EDA is ready to make use of MI research

5. © 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com Paripath MI Platform Mathematics Algebra Calculus Statistics Probability CAD Infrastructure Python TensorFlow Numpy Pyplot TCL CLI C++ EDA Algorithms Jupyter Google Colab … Proprietary Guna PASER Community apps Third party apps

6. © 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com VLSI Cell Classification Train a machine learning model to classify the type of IP with a fully extracted netlist. Char Case Study: VCC Classification IP Classifier Graph Matrix Circuit graph

7. © 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com  VCC is a common design automation problem used for detecting patterns in ASIC, AMS and Custom flows.  It is a pattern detection algorithm in its generalized form and applicable to many other problems including DRC, Extraction, Circuit Simulation and others. VCC Applications

9. © 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com GUNA: MI enabled Char Platform ❏ Guna is built on Paripath’s MI platform to offer machine learning in the cloud with VCC and other MI features. ❏ VCC repository dramatically improves ease of on pre- characterized cells. ❏ Helps with setup for re-characterization, multiple corners new process nodes. ❏ VCC feature adds to QA confidence of models.

10. © 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com MIDA: Challenges Clear Value Proposition Use Model Data Engineering High Dimensionality MI Technology Selection Integration into Legacy Systems Acceptance of Probabilistic Results Design Automation

11. © 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com MIDA: Applications  Capacitance estimation  Wire load Models estimation  RC-tree estimation  Early timing analysis  RTL power analysis  Cell and IP classification  Verification Coverage  Fast extraction  Speed up circuit simulation  Design Segmentation

12. © 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com  Guna is a ML enabled characterization platform that integrates and adds value to simulation environments.  Paripath offers community MIDA platform to quickly customize several well known MI techniques in design automation.  MI will likely change the way EDA software is written. MIDA: Summary & QA

14. © 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com  The VCC dataset consists of over 12,000 cells from multiple standard cell libraries with a variety of representative cells.  RC data was stripped from fully extracted cell netlist and resulting netlist was represented as numbers.  Cells were given 10 different labels. VCC Dataset

16. © 2018, Paripath Inc Electronic Design Process Symposium 2018 www.paripath.com Case Study: Ensemble K-nearest neighbors Bayesian networks Decision trees Boosted trees Logistic Regression Support vector machines Neural networks Random forests

Machine Intelligence for Design Automation

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