Stochastic Neural Computation I: Computational Elements
IEEE Transactions on Computers
Continuous-time analog circuits for statistical signal processing
Continuous-time analog circuits for statistical signal processing
Designing logic circuits for probabilistic computation in the presence of noise
Proceedings of the 42nd annual Design Automation Conference
Impulses and stochastic arithmetic for signal processing
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 200. on IEEE International Conference - Volume 02
Tracking Uncertainty with Probabilistic Logic Circuit Testing
IEEE Design & Test
The synthesis of robust polynomial arithmetic with stochastic logic
Proceedings of the 45th annual Design Automation Conference
AFIPS '67 (Spring) Proceedings of the April 18-20, 1967, spring joint computer conference
Stochastic computing elements and systems
AFIPS '67 (Fall) Proceedings of the November 14-16, 1967, fall joint computer conference
A reconfigurable stochastic architecture for highly reliable computing
Proceedings of the 19th ACM Great Lakes symposium on VLSI
Natively probabilistic computation
Natively probabilistic computation
Stochastic computational models for accurate reliability evaluation of logic circuits
Proceedings of the 20th symposium on Great lakes symposium on VLSI
Proceedings of the 47th Design Automation Conference
Majority-based tracking forecast memories for stochastic LDPC decoding
IEEE Transactions on Signal Processing
An Architecture for Fault-Tolerant Computation with Stochastic Logic
IEEE Transactions on Computers
Tomographic Testing and Validation of Probabilistic Circuits
ETS '11 Proceedings of the 2011 Sixteenth IEEE European Test Symposium
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Generating binary sequences for stochastic computing
IEEE Transactions on Information Theory
Architecture and statistical model of a pulse-mode digital multilayer neural network
IEEE Transactions on Neural Networks
Delayed Stochastic Decoding of LDPC Codes
IEEE Transactions on Signal Processing
Stochastic circuits for real-time image-processing applications
Proceedings of the 50th Annual Design Automation Conference
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Stochastic computing (SC) was proposed in the 1960s as a low-cost alternative to conventional binary computing. It is unique in that it represents and processes information in the form of digitized probabilities. SC employs very low-complexity arithmetic units which was a primary design concern in the past. Despite this advantage and also its inherent error tolerance, SC was seen as impractical because of very long computation times and relatively low accuracy. However, current technology trends tend to increase uncertainty in circuit behavior and imply a need to better understand, and perhaps exploit, probability in computation. This article surveys SC from a modern perspective where the small size, error resilience, and probabilistic features of SC may compete successfully with conventional methodologies in certain applications. First, we survey the literature and review the key concepts of stochastic number representation and circuit structure. We then describe the design of SC-based circuits and evaluate their advantages and disadvantages. Finally, we give examples of the potential applications of SC and discuss some practical problems that are yet to be solved.