Deterministic Finite State Machines for Stochastic Division in Unipolar Format
Nikos Temenos, Paul Peter Sotiriadis · 2020
Stochastic computing has been successfully applied in a plethora of applications, including machine learning, computer vision and soft coding/decoding, due to its low complexity, chip area, and power consumption advantages, as well as its tolerance to soft errors. Among the four fundamental numerical operations, addition, subtraction and multiplication are simple to realize stochastically. Division however is significantly more challenging and complex. This work introduces a new architecture for stochastic division in unipolar format using a deterministic finite state machine. In contrast to the existing architectures, the proposed divider does not require any internal stochastic number generator, which makes it more versatile, compact and easy to implement. The divider's accuracy is defined based on mean absolute error metrics and it is estimated using MATLAB simulation. Applications of the proposed divider in image processing are presented demonstrating its accuracy and efficiency in realistic systems.