Neural Networks and Graph Algorithms with Next-Generation Processors (Conference)


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.

Authors:
Hamilton, Kathleen E. ORCiD logo [1]; Schuman, Catherine D. ORCiD logo [1]; Young, Steven R. ORCiD logo [1]; Imam, Neena ORCiD logo [1]; Humble, Travis S. ORCiD logo [1]
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1468104
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: GraML Workshop at IPDPS 2018 - Vancouver, , Canada - 5/21/2018 8:00:00 AM-5/25/2018 4:00:00 AM
Country of Publication:
United States
Language:
English
Hamilton, Kathleen E., Schuman, Catherine D., Young, Steven R., Imam, Neena, and Humble, Travis S. Neural Networks and Graph Algorithms with Next-Generation Processors. United States: N. p., 2018. Web. doi:10.1109/IPDPSW.2018.00184.
Hamilton, Kathleen E., Schuman, Catherine D., Young, Steven R., Imam, Neena, & Humble, Travis S. Neural Networks and Graph Algorithms with Next-Generation Processors. United States. doi:10.1109/IPDPSW.2018.00184.
Hamilton, Kathleen E., Schuman, Catherine D., Young, Steven R., Imam, Neena, and Humble, Travis S. Tue . "Neural Networks and Graph Algorithms with Next-Generation Processors". United States. doi:10.1109/IPDPSW.2018.00184. https://www.osti.gov/servlets/purl/1468104.
@article{osti_1468104, title = {Neural Networks and Graph Algorithms with Next-Generation Processors}, author = {Hamilton, Kathleen E. and Schuman, Catherine D. and Young, Steven R. and Imam, Neena and Humble, Travis S.}, abstractNote = {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.}, doi = {10.1109/IPDPSW.2018.00184}, journal = {}, number = , volume = , place = {United States}, year = {Tue May 01 00:00:00 EDT 2018}, month = {Tue May 01 00:00:00 EDT 2018}

}