State-of-the-Art in DataFlow SuperComputing for BigData DeepAnalytics and SignalProcessing

The tutorial on DataFlow programming, analyses the essence of DataFlow SuperComputing, defines its advantages and sheds light on the related programming model that corresponds to the recent Intel patent about the future Intel's dataflow processor. The stress is on issues of interest for Signal Processing. The 90-minute tutorial explains the programming paradigm details, using Maxeler as an example and sheds light on the ongoing research, which, in the case of the speaker, was highly influenced by four different Nobel Laureates: (a) from Richard Feynman it was learned that future computing paradigms will be successful only if the amount of data communications is minimized; (b) from Ilya Prigogine it was learned that the entropy of a computing system would be minimized if spatial and temporal data get decoupled; (c) from Daniel Kahneman it was learned that the system software should offer options related to approximate computing; and (d) from Tim Hunt it was learned that the system software should be able to trade between latency and precision. This tutorial also includes hands-on opportunities for attendees.

Speaker: Professor Veljko Milutinovic
Adjunct Professor of Computer Science at Indiana University in Bloomington, IND, USA, Life Member of the ACM, Life Fellow of the IEEE, Member, a Former Trustee and Treasurer, of Academia Europaea, Founding Member of the Serbian National Academy of Engineering, Foreign Member of the Montenegro National Academy of Sciences and Arts

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