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In this study, we propose a novel computing paradigm "Bit Stream Computing"that is constructed on the logic used in stochastic computing, but does notnecessarily employ randomly or Binomially distributed bit streams as stochasticcomputing does. Any type of streams can be used either stochastic ordeterministic. The proposed paradigm benefits from the area advantage ofstochastic logic and the accuracy advantage of conventional binary logic. Weimplement accurate arithmetic multiplier and adder circuits, classified asasynchronous or synchronous; we also consider their suitability of processingsuccessive streams. The proposed circuits are simulated both in gate level andin transistor level with AMS 0.35um CMOS technology to show the circuits'potential for practical use. We thoroughly compare the proposed adders andmultipliers with their predecessors in the literature, individually and in aneural network application. Comparisons made in terms of area and accuracyclearly favor the proposed designs. We believe that this study opens up newhorizons for computing that enables us to implement much smaller yet accuratearithmetic circuits compared to the conventional binary and stochastic ones.

From Stochastic to Bit Stream Computing: Accurate Implementation of Arithmetic Circuits and Applications in Neural Networks