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A Stochastic Computing Architecture for Iterative Estimation
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DOI：10.1109/TCSII.2019.2916305.png)
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Abstract
En 中文
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.
Keywords:
Estimation
Signal processing algorithms
Computer architecture
Approximation algorithms
Stochastic processes
Adders
Fault tolerance
Iterative algorithms
stochastic computing
estimation algorithms
sparse estimation
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