dc.contributor.author | James, Alex Pappachen | |
dc.contributor.author | Smagulova, Kamilya | |
dc.contributor.author | Irmanova, Aidana | |
dc.date.accessioned | 2019-09-26T06:33:48Z | |
dc.date.available | 2019-09-26T06:33:48Z | |
dc.date.issued | 2018-11-15 | |
dc.identifier.citation | Smagulova, K., Irmanova, A., & James, A. P. (2018). Low Power Near-sensor Coarse to Fine XOR based Memristive Edge Detection. In 2018 International SoC Design Conference (ISOCC). IEEE. https://doi.org/10.1109/isocc.2018.8649972 | en_US |
dc.identifier.issn | 2163-9612 | |
dc.identifier.other | 10.1109/ISOCC.2018.8649972 | |
dc.identifier.uri | http://nur.nu.edu.kz/handle/123456789/4260 | |
dc.description | https://ieeexplore.ieee.org/document/8649972 | en_US |
dc.description.abstract | In this paper, we propose XOR based memristive edge detector circuit that is integrated into a near sensor log-linear CMOS pixel. Memristor threshold logic was used to design NAND gates, which serve as a building block for XOR gates. For validation of proposed circuit functionality hardware simulation of logic gates with a pixel pair was conducted using TSMC 0.18um technology and system-level simulation of the proposed circuit using SPICE models. The proposed method operates in low power and takes a small area on chip. The power consumption of one pixel is 1.16uW and total area 36.72 um 2 without photosensing component. The power consumption of NAND circuit is 1.11pW and total area 32.4um 2 . | en_US |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.rights | Attribution-NonCommercial-ShareAlike 3.0 United States | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-sa/3.0/us/ | * |
dc.subject | Low Power | en_US |
dc.subject | Research Subject Categories::TECHNOLOGY | en_US |
dc.subject | CMOS pixel | en_US |
dc.subject | Memristive Edge Detection | en_US |
dc.title | Low Power Near-sensor Coarse to Fine XOR based Memristive Edge Detection [Article] | en_US |
dc.type | Article | en_US |
workflow.import.source | science |
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