Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89554
Title: Stochastic computing with spiking neural P systems
Authors: Wong, Ming Ming
Wong, Dennis Mou Ling
Keywords: Stochastic Computing
Membrane Computing
DRNTU::Engineering::Computer science and engineering
Issue Date: 2017
Source: Wong, M. M., & Wong, D. M. L. (2017). Stochastic computing with spiking neural P systems. Journal of Universal Computer Science, 23(7), 589-602. doi:10.3217/jucs-023-07-0589
Series/Report no.: Journal of Universal Computer Science
Abstract: This paper presents a new computational framework to address the challenges in deeply scaled technologies by implementing stochastic computing (SC) using the Spiking Neural P (SN P) Systems. SC is well known for its high fault tolerance and its ability to compute complex mathematical operations using minimal amount of resources. However, one of the key issues for SC is data correlation. This computation can be abstracted and elegantly modeled by using SN P systems where the stochastic bit-stream can be generated through the neurons spiking. Furthermore, since SN P systems are not affected by data correlations, this effectively mitigate the accuracy issue in the ordinary SC circuitry. A new stochastic scaled addition realized using SN P systems is reported at the end of this paper. Though the work is still at the early stage of investigation, we believe this study will provide insights to future IC design development.
URI: https://hdl.handle.net/10356/89554
http://hdl.handle.net/10220/46312
ISSN: 0948-695X
DOI: 10.3217/jucs-023-07-0589
Rights: © 2017 J.UCS. This paper was published in Journal of Universal Computer Science and is made available as an electronic reprint (preprint) with permission of J.UCS. The published version is available at: [http://dx.doi.org/10.3217/jucs-023-07-0589]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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