Data redundancy problems in data-flow computing and solutions implemented on the recurrent architecture
Yury I. Shikunov, Yu. A. Stepchenkov, Dmitry V. Khilko, Dmitry Shikunov · 2017
This paper covers one of the main disadvantages of data-flow computational model - high overhead costs associated with storing and processing large volumes of tag data. Overcoming this disadvantage using various ways of data compression comes with a number of problems described in this article. To solve given problems new recurrent data-flow computational model was created as well as architecture based on that model. This paper describes key features and mechanics of the new model and architecture allowing us to reduce data redundancy in memory storage. Efficiency of developed mechanics is shown in implementation of fast Fourier transform algorithm.