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Dataflow matrix machines arise naturally in the context of synchronousdataflow programming with linear streams. They can be viewed as a ratherpowerful generalization of recurrent neural networks. Similarly to recurrentneural networks, large classes of dataflow matrix machines are described bymatrices of numbers, and therefore dataflow matrix machines can be synthesizedby computing their matrices. At the same time, the evidence is fairly strongthat dataflow matrix machines have sufficient expressive power to be aconvenient general-purpose programming platform. Because of the network natureof this platform, programming patterns often correspond to patterns ofconnectivity in the generalized recurrent neural networks understood asprograms. This paper explores a variety of such programming patterns.

Programming Patterns in Dataflow Matrix Machines and Generalized Recurrent Neural Nets