Application of Stochastic computing

\label{application-of-stochastic-computing}
Although there are the attractions due to the simplicity, low-cost design, faut-tolerance come from this novel computing, SC equally faces some drawbacks which cause it to be viewed a an impractical method. This is the difficulty in generating the real random sequences, because up to now, remains an open issue. However, there are lately a lot of applications concerning this kind of computation: design of digital filter (FIR, IRR) [6], design of circuits for real-time image processing applications [3], [14], [16], [15], as well as using of strochastic computational elements for neural networks [4], [5]. Those applications have partly proved the capacity and perspectives of SC in practice and future research.
Chapter 2
Stochastic Number Generator
One of the major problems in the design of stochastic computing system is the synthesis of hard- ware system for the generation of sequences of independent, uniformly distributed, random numbers. Early forms of random number generators for SC used physical noise sources. However, there are the disavantages of such random sources [19]:
Hence, the alternative approaches to random number generation, which ensure that the generated numbers must be uniformly distributed across the interval [0, 1], have been proposed. In our work, LFSR and CA will be considered as suitable solutions for generating random numbers.
About SNG, we will investigate two group of methods: SNG using comparator and SNG without using comparator. The evaluation and comparison of these approaches will be carried out to provide a deep understanding on this important problem in SC.
  1. Pseudo–random Number generator

    \label{pseudorandom-number-generator}
    1. LFSR–based PRNG

      \label{lfsrbased-prng}
    s(0) s(1)