  Stochastic computing (SC) is a promising candidate for fault tolerant
computing in digital circuits. We present a novel stochastic computing
estimation architecture allowing to solve a large group of estimation problems
including least squares estimation as well as sparse estimation. This allows
utilizing the high fault tolerance of stochastic computing for implementing
estimation algorithms. The presented architecture is based on the recently
proposed linearized-Bregman-based Sparse Kaczmarz algorithm. To realize this
architecture, we develop a shrink function in stochastic computing and
analytically describe its error probability. We compare the stochastic
computing architecture to a fixed-point binary implementation and present
bit-true simulation results as well as synthesis results demonstrating the
feasibility of the proposed architecture for practical implementation.
