Lessons from an Emerging Technology: Superconducting …Nov 30, 2017 · 2017-11, Holmes, Lessons...
Transcript of Lessons from an Emerging Technology: Superconducting …Nov 30, 2017 · 2017-11, Holmes, Lessons...
Lessons from an Emerging Technology:Superconducting Computing
Dr. D. Scott Holmes
Booz Allen Hamilton, IARPA contractor
2017-11-30
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2017-11, Holmes, Lessons from an Emerging Technology: Superconducting Computing
Future Supercomputing Vision
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Hybrid technologies: digital (CMOS, SFQ), probabilistic, analog, neuromorphic, reversible, and quantum computing (QC) — whatever works best!
SFQ digital platform supports multiple cryogenic technologies
Requires optical interconnects between room temperature and cryogenic nodes
Courtesy of the Oak Ridge National Laboratory, U.S. Department of Energy
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2017-11, Holmes, Lessons from an Emerging Technology: Superconducting Computing
Superconducting Computing Approach
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Low temperature operation (~4 K)Allows different physics
Commercially available refrigeration
LogicSFQ (Single Flux Quantum)
Switching energy ~ 2x10-20 J
Memorycompatible with SFQ logic
InterconnectsSuperconducting in the cold space
Input/Output: electrical or optical
Major energy reductions in all 3 areas!
~2 mV
~1 ps
~c/3, nearly lossless
S
F (soft)IF (hard)
S0
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Notional Prototype, IARPA C3 Program
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Metric Goal
Clock rate for superconducting logic
10 GHz
Throughput (bit-op/s) 1013
Efficiency @ 4 K (bit-op/J)
1015
CPU count 1
Word size (bit) 64
Parallel Accelerator count
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Main Memory (B) 228
Input/Output (bit/s) 109
Cryogenic Refrigerator
Input/Output
Input/Output
~ 4 K (-270 oC)
Host
CPU
Main Memory
Cache
Parallel
Accelator
PA Controller
Room
Temperature
www.iarpa.gov/index.php/research-programs/c3
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2017-11, Holmes, Lessons from an Emerging Technology: Superconducting Computing
Status of Superconductor Electronics
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Presented in this workshop:
IARPA Programs for Superconducting ComputingMarc Manheimer, IARPA
Energy efficient, high bandwidth digital data links between 4 and 300 KDr. Deborah Van Vechten, ONR
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2017-11, Holmes, Lessons from an Emerging Technology: Superconducting Computing
Digital-RF Receiver (Hypres)
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Commercial product with applications in:
Software-defined radio, satellite communications
Directly digitizes RF (no analog down-conversion)
Ultra-wide bandwidth, multi-band, multi-carrier
Hybrid temperature heterogeneous technology
Different technologies between ambient and 4 K
Closed-cycle cryogenic refrigerator48 in.
rack
Gupta, et al., IEEE Trans. Appl. Supercond., June 2011
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2017-11, Holmes, Lessons from an Emerging Technology: Superconducting Computing
Quantum Annealing (D-Wave Systems)
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D-Wave® TwoXTM (2015 August 20), a commercial superconducting quantum annealing processor
128,000 Josephson junctions
1000 qubit array
15-20 mK operating temperature
D-Wave® TwoXTM quantum annealing processor“Washington” chip
2017-11, Holmes, Lessons from an Emerging Technology: Superconducting Computing
Electronics Technology Roadmaps
- 1993-1997 NTRS: National Technology Roadmap for Semiconductors
- 1998-2013 ITRS: International Technology Roadmap for Semiconductors
• Applied Moore’s Law to integrated circuits
• Physical scaling worked until about 2004, then cores, 3D, …
• 2010: First selection of post-CMOS devices
- 2014-2015 ITRS 2.0• Driver changed from scaling to applications
• 2015: Post-CMOS map of devices
- 2016+ IRDS: International Roadmap for Devices and Systems
• Opened the door to non-semiconductortechnologies
• 2017: First roadmapsunder development
8Paolo Gargini
IRDS Chair
2017-11, Holmes, Lessons from an Emerging Technology: Superconducting Computing
IRDS BC Chapter Organization
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Beyond CMOS
Emerging memory and
storage devices
Emerging logicand information
processing devices
Emerging application
areas
Emerging device and architecture
interfaceAssessment
• Cryogenic electronics
• Emerging devices for hardware security
• Memory devices• Selector devices• Storage class
memory devices
• CMOS extension • Beyond-CMOS
charge-based• Beyond-CMOS
non-charge-based
• Map Emerging architecture to suitable devices
• Define FOMs and key challenges
• Define criteria• Based on the
quantitative benchmarking reported by NRI
Matt Marinella
Name of person-in-charge
Shamik Das
Scott HolmesErik DeBenedictis
Mike Niemier
Mike FrankPaul FranzonMatt MarinellaGeoff Burr
An Chen
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Energy–Delay Metrics: Wiring▸ RQL : Reciprocal Quantum Logic,
a superconducting single flux quantum (SFQ) logic, Jc = 100 µA/µm2
- JTL: Josephson transmission line(0.13 fJ/bit, 5.5 ps)
- PTL: passive transmission line(0.26 fJ/bit 0.0120 mm, 6.5 ps)
▸ 4.2 K operation; energy per bit at room temperature with 1000 W/W refrigeration(range I : 400–10,000 W/W)
▸ Source for RQL data:Dorojevets, Chen, Ayala, Kasperek, “Towards 32-bit Energy-Efficient Superconductor RQL Processors: The Cell-Level Design and Analysis of Key Processing and On-Chip Storage Units,” IEEE Trans. Appl. Supercond., 2015. doi: 10.1109/TASC.2014.2368354 (Fig. 1)+ private communication for delays
▸ Added to:Pan, Chang, Naeemi, “Performance analyses and benchmarking for spintronic devices and interconnects,” 2016 IEEE International Interconnect Technology Conference / Advanced Metallization Conference (IITC/AMC), San Jose, CA, 2016. doi: 10.1109/IITC-AMC.2016.7507679
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RQL (PTL)
RQL (JTL)
Superconducting
Fig. 5. Comparison between CMOS and spintronic devices in terms of (a) wire energy versus delay
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Can superconducting computing compete?
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Shadow of the Colossus/
Wander to Kyozō
SCEI
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A better way to view the relationship
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Lessons from an Emerging Technology
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Fair metrics are needed to evaluate alternative computing technologies level the playing field to allow different technologies to compete
relevant lessons from hiring for diversity?
Ramping up requires time and resources the real Moore’s Law
Government funding alone is not sufficientcost to develop energy-efficient, large-scale computers is large
ramp up using smaller products and markets
Don’t go it alone use your mother elephant
Go big or go home! small improvements are not worth the effort
large disruptions require even larger advantages
?
?
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2017-11, Holmes, Lessons from an Emerging Technology: Superconducting Computing
References
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D.S. Holmes, A.L. Ripple, and M.A. Manheimer, “Energy-efficient superconducting computing – power budgets and requirements”, IEEE Trans. Appl. Supercond., vol. 23, no. 3, pp. 1701610, June 2013. DOI: 10.1109/TASC.2013.2244634
D.S. Holmes, "Superconducting computing: Lessons from an emerging technology," 2015 Fourth Berkeley Symposium on Energy Efficient Electronic Systems (E3S), Berkeley, CA, 2015. DOI: 10.1109/E3S.2015.7336778Video online: https://www.youtube.com/watch?v=3Whh9VXHqOQ
M.A. Manheimer, “Cryogenic Computing Complexity Program: Phase 1 Introduction”, IEEE Trans. Appl. Supercond., vol.25, 1301704, June 2015, DOI: 10.1109/TASC.2015.2399866
D.S. Holmes, A.M. Kadin, M.W. Johnson, “Superconducting Computing in Large-Scale Hybrid Systems”, Computer, vol. 48, pp. 34-42, December 2015. DOI: 10.1109/MC.2015.375
International Roadmap for Devices and Systems (IRDS), http://irds.ieee.org/
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Computer, vol. 48, Dec. 2015