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Seeding Algorithm for Bipolar Stochastic Computing for Polynomial Approximations
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DOI：10.1109/LES.2025.3565235.png)
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Abstract
En 中文
Application of stochastic computing (SC) for reckoning trascendental functions as tanh(x) , so used as an activation function in convolutional neural networks is an active research area. Currently, most of the works for computing functions via SC are based on the unipolar encoding format (x is an element of[0,1]) , due to this, a method based on bipolar encoding format (x is an element of[-1,1]) is here proposed with the goal of reducing the implementation complexity, and the correlation between stochastic bitstreams. For that, a collection of existing methods is adapted for the purpose of this letter. Moreover, for reducing correlation between bitstream, an algorithm is proposed for the selection of different seeds for distinct linear feedback shift registers that yields to a low MSE. The seed selection along with the adaptation of methods for implementing polynomials with SC digital circuits based on a bipolar encoding format yields to more accurate results. Simulations were carried out for the polynomial approximation of several functions. Function tanh(x) was compared with an existing solution, verifying in that way the superior performance of the proposed approach.
Keywords:
Polynomials
Binary sequences
Encoding
Multiplexing
Logic gates
Computer architecture
Correlation
Accuracy
Probabilistic logic
Proposals
Bipolar encoding format
correlation
function approximation
polynomial computing
stochastic computing (SC)
Journal
IF:
2
Papers:
101
Citations:
696
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Cited Papers
Effect of LFSR Seeding, Scrambling and Feedback Polynomial on Stochastic Computing Accuracy.png)
Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE)
Proceedings of the 2016 Design, Automation & Test in Europe Conference & Exhibition (DATE)IF0
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