W07 Workshop on Machine Learning for CAD

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Date: 
2019-03-29
Time: 
08:30-17:30
Location / Room: 
Room 7

Organisers

Hussam Amrouch, Karlsruhe Institute of Technology (KIT), DE (Contact Hussam Amrouch)
Jörg Henkel, Karlsruher Institut für Technologie (KIT), DE (Contact Jörg Henkel)
Marilyn Wolf, Georgia Tech, US (Contact Marilyn Wolf)

Workshop Focus

This workshop focuses on machine learning methods for all aspects of CAD and electronic system design. Advances in machine learning (ML) over the past half-dozen years have revolutionized the effectiveness of ML for a variety of applications. However, design processes present challenges that require parallel advances in ML and CAD as compared to traditional ML applications such as image classification. As such, the purpose of the workshop is to discuss, define and provide a roadmap for the special needs for ML for CAD where CAD is broadly defined as design time techniques as well as run-time techniques. The results of the workshop will be published in a special issue at ACM Transactions on Design Automation of Electronic Systems (TODAES).

Invited Speakers and Panelists

  • Krishnendu Chakrabarty; Duke University
  • Rolf Drechsler; University of Bremen
  • Paul Franzon; North Carolina State University
  • Siddharth Garg; New York University
  • Georges Gielen; KU Leuven
  • Rajesh Gupta; UC San Diego
  • Ulf Schlichtmann; TU Munich
  • Dimitrios Soudris; University of Athens
  • Norbert Wehn; TU Kaiserslautern
  • Kai-Chiang Wu; National Chiao Tung University, Taiwan
  • Lilia Zaourar; CEA-LIST

Agenda

Time

Label

Session

07:30

W07.1

Registration Desk Opens

08:30

W07.2

Design Space Exploration using Machine Learning
08:30-09:00: Ulf Schlichtmann, TU Munich, Title: Machine Learning Approaches for Efficient Design Space Exploration of Application-specific NoCs (abstract).
09:00-09:30: Lilia Zaourar, CEA-LIST, Title: Machine Learning for Design Space Exploration of CPS (abstract).
09:30-10:00: Rajesh Gupta, UC San Diego,Title: TBA

10:00

W07.3

Morning Coffee Break

10:30

W07.4

Design for Reliability using Machine Learning
10:30-11:00: Rolf Drechsler, Univ. of Bremen, Title: Resilience Evaluation for Approximating SystemC Designs Using Machine Learning Techniques (abstract).
11:00-11:30: Siddharth Garg, New York University, Title: ML4TPU: Machine Learning for Energy Efficient and Reliable ML Hardware (abstract).
11:30-12:00: Kai-Chiang, National Chiao Tung University, Title: Learning-based methodologies for assessing chip health in terms of aging and reliability (abstract).

12:00

W07.5

Lunch Break

13:00

W07.6

Design-Time and Run-Time Machine Learning Techniques for SoCs
13:00-13:30: Paul Franzon, North Carolina State University, Title: Designing Your Circuits with Robots (abstract).
13:30-14:00: Krishnendu Chakrabarty, Duke University, Title: Predictive Analytics for Run-Time Anomaly Detection and Failure Prediction in Complex Core Routers (abstract).
14:30-15:00: Dimitrios Soudris, Univ. of Athens, Title: A Methodology for Application Implementation onto 3-D FPGAs (abstract).

14:30

W07.7

Afternoon Coffee Break

15:00

W07.8

Machine Learning for Circuit Modeling
15:00-15:30: Georges Gielen, KU Leuven, Title: AI learning techniques in design and test of analog integrated circuits (abstract).
15:30-16:00: Norbert Wehn, TU Kaiserslautern, Title: Modeling of DRAM Behavior with Recurrent Neural Networks (abstract).
16:00-16:15: (Poster) Lukas Sekanina, Title: Machine Learning in Logic Synthesis.
16:15-16:30: (Poster) Huiyuan Song, Title: Fast FPGA Routing Algorithm using Graph Neural Network.

16:30

W07.9

Panel: Where Do We Go From Here?
Panelists: Paul Franzon, Rajesh Gupta, Nicholas Ventroux, Norbert Wehn
Moderator: Marilyn Wolf

17:30

W07.10

Workshop Wrap-up

18:30

Joint dinner with discussion of future plans (buy-your-own-meal)
Location: Ristorante Buca Mario (google maps)

Agenda

TimeLabelSession
07:30W07.1Registration Desk opens
08:30W07.2Workshops start
10:00W07.3Coffee break 1
12:00W07.4Lunch break
14:30W07.5Coffee break 2
17:30W07.6Workshops end