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Implementing Deep Neural Networks on Fresh Breeze

This paper discusses how deep neural network simulations can be implemented and executed on a proposed computer system inspired by data flow principles. In particular, we use the Kiva simulator developed by the MIT CSAIL Fresh Breeze project to simulate the data flow machine used for running the neural networks. An example of a deep neural network and its simulation using Kiva is presented. The neural network is written in funJava, a functional version of Java. Our compiler for funJava programs converts funJava methods to data flow graphs, identifies opportunities for data parallel implementation, and generates a graph of machine executable code blocks called codelets which are executed as tasks by the data flow machine. The behavior and performance of the neural network as the input data size and the number of cores in the data flow machine are varied are reported and discussed.