extreme scale design automation€¦ · challenges and opportunities for 2025 and beyond r. iris...

EXTREME SCALE DESIGN AUTOMATION CCC/SIGDA Workshop Series

Alex K. Jones

University of Pittsburgh

Special Session Presentation

SiGdaspecial interest group on

design automation

Distribution A – Approved for Public Release; Distribution Unlimited

Classic Moore’s Law: Made New Designs Possible, Old Ones Lucrative

Silicon Process Technology

Intel386™ DX

Processor

Intel486™ DX

Processor

Pentium® Processor

Pentium® II, III

Processors

Pentium® 4

1.5µ 1.0µ 0.8µ 0.6µ 0.35µ 0.25µ 0.18µ

Time

30 engineers, ½ yr

500+ engineers, 5 years 5

Source: Bob Colwell Intel, DARPA

SURVEY: WILL MOORE’S LAW END?

Source: EE Times Survey March 2014

•  Traditional (Dennard) scaling ended 10 years ago (sub 80nm)

•  Industry roadmap will continue to find patches to silicon (5nm) –  Seems the cost scaling

proposition may have ended

EDA is sDll chasing a mul$-‐dimensional moving target


design automation

EXTREME SCALE SILICON ROADMAP

EUV Saves Costs versus Double Patterning

450mm Increases Throughput to Save Cost

2023?

DELAYED FD-‐SOI?

All around gate transistors?

Will scale to 10nm? 7nm? 5nm?

What is clear? EDA will become increasingly important

at the Extreme Scale


design automation

CCC/SIGDA WORKSHOPS ON EXTREME SCALE DESIGN AUTOMATION

Series of three workshops:

•  Workshop 1: Emerging Technologies and Workforce Continuity –  March 7-8, 2013 Pittsburgh

•  Workshop 2: Extreme Scale Chips and Industry Research –  June 2-3, 2013 Austin (Collocated with DAC)

•  Workshop 3: Achieving Sustainable Collaborations Through Abstractions, Methodologies, and Benchmarks –  February 20-21, 2014 Tampa

The purpose of this workshop series was to take an introspec8ve look at the EDA field while crystalizing

a vision for both the near and long term.


design automation

WHERE SHOULD EDA INVEST?

Major Focus Areas

•  Extreme-scale Electronic Design Automation (ESDA) –  First Big Data Discipline, 1015 devices –  Focus on System-level Design and Verification

•  Emerging Technologies (Post-CMOS/Hybrid) –  Develop Full System Flows –  Technology choices: more than ad hoc demonstrations

•  New Markets (DAoT) –  Near term: CPS/IoT, Cyber-secure systems (hardware) –  Medium term: Biology/Medical Technology

•  Cross Cutting –  Abstractions,

Metrics, Benchmarks

–  Synergy with Computer Architecture

–  Education and Workforce


design automation

EXTREME SCALE DESIGN AUTOMATION Continuing Onward: Next-Generation Electronic Systems


design automation

ESDA IS AN EXCITING TIME FOR EDA

EDA’s Objectives “at a glance” ITRS Productivity Curve

Figure 1: The design productivity gap [6]

198

119

85

198

9

1993

199

720

01

200

5

200

9

2013

2017

2021

2025

2029 Time

Technology Capabilities2x/36 months

HW Design ProductivityFilling with IP and Memory

HW Design Productivity

HW Design Gap

LogGates/Chip

Gates/Day

Figure 3: Key aspects of modern research and development in EDA.

Level of Abstraction

Quality (of optimization) of result = QOR; PPAY= power, performance, area and yield

Paradigm domain-specific, stochastic and approximate computing, etcApplication

Time-scale of the research lifecycle in years How are we doing?

EDA Improvement PotenDal

This uncertain, and exci8ng environment is reminiscent of the beginnings of the EDA era prior to the stability provided by Dennard

scaling.


design automation

Uncertainty of the current environment is alleviated by the large global demand for IC

products

Figure 1: The design productivity gap [6]

1981

1985

1989

1993

1997

2001

2005

2009

2013

2017

2021

2025

2029 Time

Technology Capabilities2x/36 months

HW Design ProductivityFilling with IP and Memory

HW Design Productivity

HW Design Gap

LogGates/Chip

Gates/Day

WHAT ABOUT “LEGACY” TECHNOLOGY NODES?

130nm design starts dominate the market

•  Barriers –  “Good enough solutions” –  Fear new methods break tools

•  Unintended side effects •  Push button flows still achievable

–  Inadequate investment

•  130nm designs are popular –  Reduces upfront cost –  Increases yield

Closing the produc$vity gap at 130nm could approximate

current technology capabili$es

An EDA advancement demonstrated in a tool that finds superior solu8ons is par8cularly valuable to effec8vely u8lize legacy technology nodes. SiGdaspecial interest group on

design automation

HYBRID POST-CMOS ELECTRONICS A Changing Landscape: New Technologies for Integrated Circuits


design automation

ROLE OF EMERGING TECHNOLOGIES

Replace Si-CMOS? •  There are many emerging

technologies –  Most are niche –  Likely hybrid solutions

•  Challenges –  Integration –  Stochastic behavior –  Models and

abstractions –  Full system

demonstrations

11

processed in order to evaluate and understand low-level (e.g., atomistic) behavior and to explore all reachable system states is huge. There may need to be better statistical methods for simulations involving big data.

Flexible Models: There will be a need for more flexible, modular, and/or extendable tools that can easily incorporate different behavior from various emerging technologies. A building-block approach, where each block is easily modifiable may be particularly advantageous.

High-level Abstractions: The benefits of high-level prototyping, estimation, synthesis, and verification can only be unlocked with appropriate high-level design and EDA abstractions. Some emerging technologies are particularly dependent on new abstractions and models–from logic to system level.

Physical Layout: New devices and circuit styles typically require rethinking traditional notions of physical layout, synthesis, extraction, and verification.

3.2 New Design Processes for New Technologies

Focusing on high-level analysis may require rethinking how systems are specified, designed, and implemented. If we take a current system that has been optimally designed using traditional silicon technology, and then simply replace each silicon component with an emerging-technology-equivalent, it may not yield any significant benefits (and may even be worse). This may not be surprising, since design decisions at the highest level are often the result of constraints dictated at the technology level. Instead, if we have the capacity to accomplish design exploration at the highest level first, unconstrained by technology, we may end up with a completely new way of computing that could allow us to better exploit the best properties of new technologies. Only then will it become apparent what kind of design automation tools would be most beneficial for these new technologies. We need new models that give designers the flexibility to change the high-level structure.

TECHNOLOGY PROPOSED USEOptical interconnect, optical devices High-performance, high-bandwidth communications

Terahertz (RF) circuitsAutomotive radar, security, high-bandwidth wireless communcation

Microelectromechanical systems (MEMS) and Nanoelectromechanical (NEMS)

Mechanical filter/switches, wideband antennas, gyroscopres, energy harvesting, data storage, sensors

Spintronics/multiferroicsModeling synapse, physical brain/biomimetric behavior

Flexible electronicsWearable computing, body tracking, glucose monitoring

Qbit technologies Quantum computing/annealing/optimization

Phase-change memories (including memristors) DNA memory (having long retention)

Microfludicis Lab-on-a-chip, cooling

Steep slope devices Ultra low-power computing

Superconductors Ultra low-power computing, ultra high performance

Carbon-based electronicsUltra low-power computing, high performance, monolithic 3D ICs

Figure 4: Emerging technologies for the EDA community.

EDA should not determine which new technologies to pursue.

EDA should address new technology specific challenges for EDA flows.


design automation

THE HYPE CYCLE OF INNOVATION

Expectations

time

InnovationTrigger

Expectations

First-generationproducts, high price, lots of customization needed

time

InnovationTrigger

Expectations

Mass mediaHype begins


time

InnovationTrigger

Expectations

Mass mediaHype begins


time

InnovationTrigger

Peak of InflatedExpectations

Expectations

Negative press beginsMass media

Hype begins


time

InnovationTrigger


Expectations


Hype begins


time

InnovationTrigger


Trough ofDisillusionment

Expectations


Hype begins


Second-generationproducts, some services

time

InnovationTrigger


Trough ofDisillusionment

Expectations


Hype begins



time

InnovationTrigger


Trough ofDisillusionment Slope of Enlightenment

Expectations


Hype begins


Methodologies and best practices developing


time

InnovationTrigger



Expectations


Hype begins



Third-generationproducts, out of thebox solutions, product suitesSecond-generation

products, some services

time

InnovationTrigger



Expectations


Hype begins





time

InnovationTrigger



Plateau ofProductivity

Expectations


Hype begins



High-growth adoptionphase starts: 20 to 30percent adoption



time

InnovationTrigger



Plateau ofProductivity

Source: Gartner

Generalized Pattern of Research and Commercialization From Conception to Productive Use

EDA Minimizes the Trough of Disillusionment

EDA Maximizes the Slope of Enlightenment

EDA Can Lead to Concrete Evalua$on of New

Technologies

NSF NEB Program–EDA should be included


design automation

THE DESIGN AUTOMATION OF THINGS Looking Forward: New Markets and Applications


design automation

THE POWER OF DESIGN AUTOMATION

Success of Electronic DA •  Facilitated unprecedented

exponential advancement of Si/CMOS

•  EDA separates –  Design –  Construction –  Optimization

•  Clear abstraction & predictive models of low-level behavior allows: –  High level analysis –  Optimization –  Verification

DA Techniques Beneficial to Areas •  Require analysis before

construction •  Lack appropriate abstractions •  Rely on both optimization and

analysis to meet specs •  Can make low cost alternatives to

existing high cost products •  Need efficient assessment of

outcomes prior to construction

Biological and Medical Technologies

Cyber-‐physical Systems

Cyber-‐secure Systems


design automation

NEW MARKETS WHERE DESIGN AUTOMATION CAN BE TRANSFERRED

Identified Potential

CPS/loT

Automotive Energy

Robotics

Medical Technology

High Barrier

DFS/Verification

Emerging

DF Wearables

Low Barrier


High Barrier

Emerging

Low Barrier


CPS/loT

High Barrier

Emerging

Low Barrier


CPS/loT

Automotive

High Barrier

Emerging

Low Barrier


CPS/loT

Automotive Energy

High Barrier

Emerging

Low Barrier


CPS/loT

Automotive Energy

Robotics

High Barrier

Emerging

Low Barrier


CPS/loT

Automotive Energy

Robotics

High Barrier

Emerging

DF Wearables

Low Barrier


CPS/loT

Automotive Energy

Robotics

High Barrier

DFS/Verification

Emerging

DF Wearables

Low Barrier


design automation

NSF CPS– EDA iden8fied but missing

NSF STARSS– EDA also missing

NSF EFRI– BioDA poten8al

topic

CROSS CUTTING CHALLENGES Abstractions, Metrics, and Benchmarks

Education and Workforce Synergies with Computer Architecture


design automation


design automation

EDUCATING IN EDA “EDA? But I want to save the planet!”

“EDA? But I want to do something cool!”

•  Students want to impact society

•  EDA is not perceived to “change the world” •  EDA is TWO levels of

indirecDon away from “cool”

“EDA? That’s too hard! I have to learn physics and algorithms and

stuff!”

“What do you mean, build the tools to make the chips that enable the

smartphone? Can’t I just write apps for Google?”

Industry University Partnerships!

Ac8vity creates excitement

CURRICULUM AND STRATEGY

•  I teach the same old courses the same way, it works!

•  Students can’t learn this stuff before they know “the basics”

•  Students will see the importance of MY course

•  We should call this course what it is: “formal methods of verificaDon” X BeZer marke8ng to

students

Make EDA fun (e.g., Crowdsourcing)

Cri8cal Mass & MOOCs


design automation

CONCLUSIONS


design automation

DIMENSIONS OF FUTURE EDA ACTIVITIES

Electronics: Hybrid CMOS with Emerging

Technologies

New Markets:Cyber-physical, Cyber-secure, and Bio-medical Technologies

Traditional EDA Tool-kitImmediate Need: EDA for

scaled CMOS + product ready tech

Immediate Need: EDA applied to near fields – automotive,

robotics, and energy

EDA Approaches on Big Data

Transformative: Big data research–system level design and verification

Transformative: EDA big-data methodologies applied to far

fields – synthetic bio, systems bio, medical devices


Technologies


Technologies



Technologies


Traditional EDA Tool-kit


Technologies





Technologies







Technologies








Technologies







Transformative: Big data research–system level design and verification


design automation

ORGANIZERS

Alex Jones – Pim Iris Bahar -‐ Brown Srinivas Katkoori -‐ USF

Patrick Madden – Binghamton Diana Marculescu -‐ CMU Igor Markov -‐ Michigan


design automation

PARTICIPANTS AND CONTRIBUTORS R. Iris Bahar, Sankar Basu, Sanjukta Bhanja, Randy Bryant, Paul Bunyk, Krish Chakrabarty, Yiran Chen, Derek Chiou, Bob Colwell, Andre DeHon, Sujit Dey, Alex Doboli, Nik Dutt, Dale Edwards, Jim Faeder, Richard Goering, Patrick Groeneveld, Ian Harris, Mark Johnson, Alex Jones, Bill Joyner, Ramesh Karri, Srinivas Katkoori, Selcuk Kose, Steve Levitan, Hai Li, Xin Li, Patrick Madden, Diana Marculescu, Radu Marculescu, Igor L. Markov, Pinaki Mazumder, Mac McNamara, Noel Menezes, Prabhat Mishra, Natasa Miskov-Zivanov, Kartik Mohanram, Vijay Narayanan, Sani Nassif, John Nestor, David Pan, Mandy Pant, Sudeep Pasricha, Rob Rutenbar, Sachin Sapatnekar, Lou Scheffer, Carl Sechen, Don Thomas, Josep Torrellas, Jacob White, Mehmet C. Yildiz.


design automation

FINAL REPORT AVAILABLE

Workshops on Extreme Scale Design Automation (ESDA) Challenges and Opportunities for 2025 and Beyond

R. Iris Bahar, Alex K. Jones, Srinivas Katkoori, Patrick H. Madden, Diana Marculescu, and Igor L. Markov

AbstractIntegrated circuits and electronic systems, as well as design technologies, are evolving at a great rate—both quantitatively and qualitatively. Major developments include new interconnects and switching devices with atomic-scale uncertainty, the depth and scale of on-chip integration, electronic system-level integration, the increasing significance of software, as well as more effective means of design entry, compilation, algorithmic optimization, numerical simulation, pre- and post-silicon design validation, and chip test. Application targets and key markets are also shifting substantially from desktop CPUs to mobile platforms to an Internet-of-Things infrastructure. In light of these changes in electronic design contexts and given EDA’s significant dependence on such context, the EDA community must adapt to these changes and focus on the opportunities for research and commercial success. The CCC workshop series on Extreme-Scale Design Automation, organized with the support of ACM SIGDA, studied challenges faced by the EDA community as well as new and exciting opportunities currently available. This document represents a summary of the findings from these meetings.

Workshop ParticipantsR. Iris Bahar, Sankar Basu, Sanjukta Bhanja, Randy Bryant, Paul Bunyk, Krish Chakrabarty, Yiran Chen, Derek Chiou, Bob Colwell, Andre DeHon, Sujit Dey, Alex Doboli, Nik Dutt, Dale Edwards, Jim Faeder, Richard Goering, Patrick Groeneveld, Ian Harris, Mark Johnson, Alex Jones, Bill Joyner, Ramesh Karri, Srinivas Katkoori, Selcuk Kose, Steve Levitan, Hai Li, Xin Li, Patrick Madden, Diana Marculescu, Radu Marculescu, Igor L. Markov, Pinaki Mazumder, Mac McNamara, Noel Menezes, Prabhat Mishra, Natasa Miskov-Zivanov, Kartik Mohanram, Vijaykrishnan Narayanan, Sani Nassif, John Nestor, David Pan, Mandy Pant, Sudeep Pasricha, Rob Rutenbar, Sachin Sapatnekar, Lou Scheffer, Carl Sechen, Don Thomas, Josep Torrellas, Jacob White, Mehmet C. Yildiz, Hao Zheng.

WORKSHOPS ON EXTREME SCALE DESIGN AUTOMATION (ESDA) CHALLENGES AND OPPORTUNITIES FOR 2025 AND BEYOND


design automation

Workshops on Extreme Scale Design Automation (ESDA) Challenges and Opportunities for 2025 and Beyond

R. Iris Bahar, Alex K. Jones, Srinivas Katkoori, Patrick H. Madden, Diana Marculescu, and Igor L. Markov

AbstractIntegrated circuits and electronic systems, as well as design technologies, are evolving at a great rate—both quantitatively and qualitatively. Major developments include new interconnects and switching devices with atomic-scale uncertainty, the depth and scale of on-chip integration, electronic system-level integration, the increasing significance of software, as well as more effective means of design entry, compilation, algorithmic optimization, numerical simulation, pre- and post-silicon design validation, and chip test. Application targets and key markets are also shifting substantially from desktop CPUs to mobile platforms to an Internet-of-Things infrastructure. In light of these changes in electronic design contexts and given EDA’s significant dependence on such context, the EDA community must adapt to these changes and focus on the opportunities for research and commercial success. The CCC workshop series on Extreme-Scale Design Automation, organized with the support of ACM SIGDA, studied challenges faced by the EDA community as well as new and exciting opportunities currently available. This document represents a summary of the findings from these meetings.

Workshop ParticipantsR. Iris Bahar, Sankar Basu, Sanjukta Bhanja, Randy Bryant, Paul Bunyk, Krish Chakrabarty, Yiran Chen, Derek Chiou, Bob Colwell, Andre DeHon, Sujit Dey, Alex Doboli, Nik Dutt, Dale Edwards, Jim Faeder, Richard Goering, Patrick Groeneveld, Ian Harris, Mark Johnson, Alex Jones, Bill Joyner, Ramesh Karri, Srinivas Katkoori, Selcuk Kose, Steve Levitan, Hai Li, Xin Li, Patrick Madden, Diana Marculescu, Radu Marculescu, Igor L. Markov, Pinaki Mazumder, Mac McNamara, Noel Menezes, Prabhat Mishra, Natasa Miskov-Zivanov, Kartik Mohanram, Vijaykrishnan Narayanan, Sani Nassif, John Nestor, David Pan, Mandy Pant, Sudeep Pasricha, Rob Rutenbar, Sachin Sapatnekar, Lou Scheffer, Carl Sechen, Don Thomas, Josep Torrellas, Jacob White, Mehmet C. Yildiz, Hao Zheng.

WORKSHOPS ON EXTREME SCALE DESIGN AUTOMATION (ESDA) CHALLENGES AND OPPORTUNITIES FOR 2025 AND BEYOND

For more informaDon hZp://www.cra.org/ccc/visioning/visioning-‐ac8vi8es/esda

THANK YOU! Alex K. Jones

University of Pittsburgh [email protected]


design automation

extreme scale design automation€¦ · challenges and opportunities for 2025 and beyond r. iris...

Documents