Design of Stochastic Machines Dedicated to Approximate Bayesian inferences

PhD thesis of Marvin Faix (2016)

The aim of this research is to design computers best suited to do probabilistic reasoning.

The focus of the research is on the processing of uncertain data and on the computation of probabilistic distribution. For this, new machine architectures are presented. The concept they are built on is different to the one proposed by Von Neumann, without any fixed or floating point arithmetic. These architectures could replace the current processors in sensor processing and robotic fields.

In this thesis, two types of probabilistic machines are presented. Their designs are radically different, but both are dedicated to Bayesian inferences and use stochastic computing.

The first deals with small-dimension inference problems and uses stochastic computing to perform the necessary operations to calculate the inference. This machine is based on the concept of probabilistic bus and has a strong parallelism.

The second machine can deal with intractable inference problems. It implements a particular MCMC method : the Gibbs algorithm at the binary level. In this case, stochastic computing is used for sampling the distribution of interest. An important feature of this machine is the ability to circumvent the convergence problems generally attributed to stochastic computing.

Finally, an extension of this second type of machine is presented. It consists of a generic and programmable machine designed to approximate solution to any inference problem.


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