Bit-Stream Processing with No Bit-Stream: Efficient Software Simulation of Stochastic Vision Machines

Sercan Aygun, M. Hassan Najafi, Mohsen Imani, Ece Olcay Gunes

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Stochastic computing (SC) is an emerging paradigm that has come to the fore in computer vision applications in the last decade. Complex arithmetic circuitry is reduced to simple logic gates, fed with uniform random bit-streams. Due to the requirement of long bit-streams, the computer-aided simulation of SC systems is facing run-time and memory-use challenges. This work presents an efficient approach for emulating SC-based systems. The proposed simulation technique does not utilize actual bit-streams but produces similar results as if the traditional stochastic bit-streams were processed. The data are processed with the aid of a correlation-controlled contingency table (CT) construct. Our technique emulates three state-of-the-art stochastic bit-streams, namely, bit-streams with binomial distribution, pseudo-random, and low-discrepancy bit-streams. We validate the proposed technique by emulating three new SC image processing designs. We propose novel SC designs for (i) template matching, (ii) image compositing, and (iii) bilinear interpolation. Our experimental results show that our simulation technique provides comparable accuracy to processing actual bit-streams, but at a significantly lower run-time and memory usage.

Original languageEnglish
Title of host publicationGLSVLSI 2023 - Proceedings of the Great Lakes Symposium on VLSI 2023
PublisherAssociation for Computing Machinery
Number of pages7
ISBN (Electronic)9798400701252
Publication statusPublished - 5 Jun 2023
Event33rd Great Lakes Symposium on VLSI, GLSVLSI 2023 - Knoxville, United States
Duration: 5 Jun 20237 Jun 2023

Publication series

NameProceedings of the ACM Great Lakes Symposium on VLSI, GLSVLSI


Conference33rd Great Lakes Symposium on VLSI, GLSVLSI 2023
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2023 ACM.


This work was supported in part by National Science Foundation (NSF) grants #2127780 and #2019511, Semiconductor Research Corporation (SRC), O ce of Naval Research, grant #N00014-21-1-2225 and #N00014-22-1-2067, the Air Force O ce of Scientific Research under award #FA9550-22-1-0253, the Louisiana Board of Regents Support Fund #LEQSF(2020-23)-RD-A-26, and generous gifts from Cisco, Xilinx, and Nvidia.

FundersFunder number
Air Force O ce of Scientific Research9550-22-1-0253
O ce of Naval Research00014-21-1-2225, 00014-22-1-2067
National Science Foundation2019511, 2127780
Semiconductor Research Corporation
Louisiana Board of Regents2020-23)-RD-A-26


    • computer vision
    • random sources
    • simulation
    • stochastic computing


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