Survey of Stochastic Computing

By Armin Alaghi, John Patrick Hayes. TECS, 2013

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Acceleration of Deep Neural Network Training with Resistive Cross-Point Devices: Design Considerations (2016, methodology): “In order to implement a local and parallel update on an array of two-terminal devices that can perform both weight storage and processing (RPU) we first propose to significantly simplify the multiplication operation itself by using stochastic computing techniques (Gaines, 1967; Poppelbaum et al., 19”
Noise as a Resource for Computation and Learning in Networks of Spiking Neurons (2014, background): “KEYWORDS | Computational power; neural networks; neuromorphic hardware; noise; self-organization; spiking neurons; stochastic computing”
Approximate computing: An emerging paradigm for energy-efficient design (2013, background): “Approximate computing, hence, employs deterministic designs that produce imprecise results.”
Stochastic Memristive Devices for Computing and Neuromorphic Applications (2013, background): “proaches this is achieved via a stochastic number generator (SNG) 24). SNGs account for the majority of the resource usage in stochastic ( 9 computing, with a cost as high as 80% of the entire system (24). The ability to generate random bitstreams using memristive devices can represent an intriguing”