Michael Lunglmayr, Daniel Wiesinger, Werner Haselmayr,
"A stochastic computing architecture for iterative estimation"
, in IEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 67, Nummer 3, IEEE, Seite(n) 580-584, 3-2020, ISSN: 1558-3791
A stochastic computing architecture for iterative estimation
Sprache des Titels:
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.
Sprache der Kurzfassung:
IEEE Transactions on Circuits and Systems II: Express Briefs