ParaLearn : a massively parallel, scalable system for learning interaction networks on FPGAs

doi:10.1145/1810085.1810100

ParaLearn: a massively parallel, scalable system for learning interaction networks on FPGAs

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Authors:	Narges Bani Asadi	Stanford University, Stanford, CA
	Christopher W. Fletcher	University of California, Berkeley, CA
	Greg Gibeling	University of California, Berkeley, CA
	Eric N. Glass	Electrical Engineering Department, Stanford University, Stanford, CA, USA 94305
	Karen Sachs	Microbiology and Immunology Department, Stanford University, Stanford, CA, USA 94305
	Daniel Burke	Electrical Engineering and Computer Sciences Department, University of California, Berkeley, CA, USA 94720
	Zoey Zhou	Electrical Engineering Department, Stanford University, Stanford, CA , USA 94305
	John Wawrzynek	University of California, Berkeley, CA
	Wing H. Wong	Stanford University, Stanford, CA
	Garry P. Nolan	Stanford University, Stanford, CA

	2010 Article
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	· Proceeding
	ICS '10 Proceedings of the 24th ACM International Conference on Supercomputing
	Pages 83-94 ACM New York, NY, USA ©2010 table of contents ISBN: 978-1-4503-0018-6 doi>10.1145/1810085.1810100

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Tags: algorithms bayesian networks design fpga markov chain monte carlo performance reconfigurable computing signal transduction networks special-purpose and application-based systems

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