Abstract
An efficient mapping scheme of Boltzmann machine computations onto a distributed-memory multiprocessor, which exploits the synchronous spatial parallelism, is presented. In this scheme, the neurons in a Boltzmann machine are partitioned into p disjoint sets, and each set is mapped on a processor of a p-processor system. Parallel convergence and learning algorithms of Boltzmann machines, the necessary communication pattern among the processors, and their time complexities when neurons are partitioned and mapped onto a distributed-memory multiprocessor are investigated. An expected p-processor speed-up of the parallelizing scheme over a single processor is also analyzed theoretically. This analysis can be used as a basis for determining the most cost-effective or optimal number of processors according to the given communication capabilities and interconnection topologies. >
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
