Agile Simulation of Stochastic Computing Image Processing with Contingency Tables

The rapid computerized simulation of stochastic computing (SC) systems is a challenging problem. A method for agile simulation of SC image processing is proposed in this work. The input operands are processed with the aid of a correlation-controlled contingency table (CT) construct without using act...

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Bibliographic Details
Published in:IEEE transactions on computer-aided design of integrated circuits and systems Vol. 42; no. 10; p. 1
Main Authors: Aygun, Sercan, Najafi, M. Hassan, Imani, Mohsen, Gunes, Ece Olcay
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Bit-stream processing
Computational modeling
Computed tomography
Computer memory
computer-aided simulation
Contingency
contingency table
Contingency tables
Correlation
Error analysis
Image processing
Integrated circuit modeling
Interpolation
Logic gates
Pseudorandom
Simulation
stochastic computing
Stochastic processes
ISSN:0278-0070, 1937-4151
Online Access:Get full text
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Summary:The rapid computerized simulation of stochastic computing (SC) systems is a challenging problem. A method for agile simulation of SC image processing is proposed in this work. The input operands are processed with the aid of a correlation-controlled contingency table (CT) construct without using actual stochastic bit-streams. The proposed approach underlines the validity of CT simulation with (i) image compositing, (ii) pattern detection, and (iii) bilinear interpolation case studies. Using the corresponding error models, we emulate the state-of-the-art pseudo-random and quasi-random bit-streams. Experimental results show that the proposed approach achieves similar computation accuracy to the traditional SC simulation while performing runtime-and memory-efficient computations. The execution time reduces more than 200× for the image compositing task when emulating random bit-streams with CT. Pattern detection and bilinear interpolation further showed 76× and 22× lower memory usage, respectively, when employing CT.
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ISSN:0278-0070
1937-4151
DOI:10.1109/TCAD.2023.3243136