TY - GEN
T1 - Neural networks and graph algorithms with next-generation processors
AU - Hamilton, Kathleen E.
AU - Schuman, Catherine D.
AU - Young, Steven R.
AU - Imam, Neena
AU - Humble, Travis S.
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/3
Y1 - 2018/8/3
N2 - The use of graphical processors for distributed computation revolutionized the field of high performance scientific computing. As the Moore's Law era of computing draws to a close, the development of non-Von Neumann systems: neuromorphic processing units, and quantum annealers; again are redefining new territory for computational methods. While these technologies are still in their nascent stages, we discuss their potential to advance computing in two domains: machine learning, and solving constraint satisfaction problems. Each of these processors utilize fundamentally different theoretical models of computation. This raises questions about how to best use them in the design and implementation of applications. While many processors are being developed with a specific domain target, the ubiquity of spin-glass models and neural networks provides an avenue for multi-functional applications. This provides hints at the future infrastructure needed to integrate many next-generation processing units into conventional high-performance computing systems.
AB - The use of graphical processors for distributed computation revolutionized the field of high performance scientific computing. As the Moore's Law era of computing draws to a close, the development of non-Von Neumann systems: neuromorphic processing units, and quantum annealers; again are redefining new territory for computational methods. While these technologies are still in their nascent stages, we discuss their potential to advance computing in two domains: machine learning, and solving constraint satisfaction problems. Each of these processors utilize fundamentally different theoretical models of computation. This raises questions about how to best use them in the design and implementation of applications. While many processors are being developed with a specific domain target, the ubiquity of spin-glass models and neural networks provides an avenue for multi-functional applications. This provides hints at the future infrastructure needed to integrate many next-generation processing units into conventional high-performance computing systems.
KW - Constraint statisfaction
KW - Deep learning
KW - Graph algorithms
KW - Neuromorphic computing
KW - Quantum computing
UR - http://www.scopus.com/inward/record.url?scp=85052236667&partnerID=8YFLogxK
U2 - 10.1109/IPDPSW.2018.00184
DO - 10.1109/IPDPSW.2018.00184
M3 - Conference contribution
AN - SCOPUS:85052236667
SN - 9781538655559
T3 - Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2018
SP - 1194
EP - 1203
BT - Proceedings - 2018 IEEE 32nd International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 32nd IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2018
Y2 - 21 May 2018 through 25 May 2018
ER -