Stochastic Computing Emulation of Memristor Cellular Nonlinear Networks
Cellular Nonlinear Networks (CNN) are a concept introduced in 1988 by Leon Chua and Lin Yang as a bio-inspired architecture capable of massively parallel computation. Since then, CNN have been enhanced by incorporating designs that incorporate memristors to profit from their processing and memory ca...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Micromachines |
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| Online Access: | https://www.mdpi.com/2072-666X/13/1/67 |
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| author | Oscar Camps Mohamad Moner Al Chawa Stavros G. Stavrinides Rodrigo Picos |
| author_facet | Oscar Camps Mohamad Moner Al Chawa Stavros G. Stavrinides Rodrigo Picos |
| author_sort | Oscar Camps |
| collection | DOAJ |
| description | Cellular Nonlinear Networks (CNN) are a concept introduced in 1988 by Leon Chua and Lin Yang as a bio-inspired architecture capable of massively parallel computation. Since then, CNN have been enhanced by incorporating designs that incorporate memristors to profit from their processing and memory capabilities. In addition, Stochastic Computing (SC) can be used to optimize the quantity of required processing elements; thus it provides a lightweight approximate computing framework, quite accurate and effective, however. In this work, we propose utilization of SC in designing and implementing a memristor-based CNN. As a proof of the proposed concept, an example of application is presented. This application combines Matlab and a FPGA in order to create the CNN. The implemented CNN was then used to perform three different real-time applications on a 512 × 512 gray-scale and a 768 × 512 color image: storage of the image, edge detection, and image sharpening. It has to be pointed out that the same CNN was used for the three different tasks, with the sole change of some programmable parameters. Results show an excellent capability with significant accompanying advantages, such as the low number of needed elements further allowing for a low cost FPGA-based system implementation, something confirming the system’s capacity for real time operation. |
| first_indexed | 2024-03-10T00:54:46Z |
| format | Article |
| id | doaj.art-621edef71d83443d8954f38edd199b0b |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-10T00:54:46Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-621edef71d83443d8954f38edd199b0b2023-11-23T14:44:21ZengMDPI AGMicromachines2072-666X2021-12-011316710.3390/mi13010067Stochastic Computing Emulation of Memristor Cellular Nonlinear NetworksOscar Camps0Mohamad Moner Al Chawa1Stavros G. Stavrinides2Rodrigo Picos3Industrial Engineering and Construction Department, University of Balearic Islands, 07122 Palma Mallorca, SpainInstitute of Circuits and Systems, Technical University of Dresden, 01062 Dresden, GermanySchool of Science and Technology, International Hellenic University, 57006 Thessaloniki, GreeceIndustrial Engineering and Construction Department, University of Balearic Islands, 07122 Palma Mallorca, SpainCellular Nonlinear Networks (CNN) are a concept introduced in 1988 by Leon Chua and Lin Yang as a bio-inspired architecture capable of massively parallel computation. Since then, CNN have been enhanced by incorporating designs that incorporate memristors to profit from their processing and memory capabilities. In addition, Stochastic Computing (SC) can be used to optimize the quantity of required processing elements; thus it provides a lightweight approximate computing framework, quite accurate and effective, however. In this work, we propose utilization of SC in designing and implementing a memristor-based CNN. As a proof of the proposed concept, an example of application is presented. This application combines Matlab and a FPGA in order to create the CNN. The implemented CNN was then used to perform three different real-time applications on a 512 × 512 gray-scale and a 768 × 512 color image: storage of the image, edge detection, and image sharpening. It has to be pointed out that the same CNN was used for the three different tasks, with the sole change of some programmable parameters. Results show an excellent capability with significant accompanying advantages, such as the low number of needed elements further allowing for a low cost FPGA-based system implementation, something confirming the system’s capacity for real time operation.https://www.mdpi.com/2072-666X/13/1/67cellular nonlinear networksstochastic logicreal time processingimage processingmemristors |
| spellingShingle | Oscar Camps Mohamad Moner Al Chawa Stavros G. Stavrinides Rodrigo Picos Stochastic Computing Emulation of Memristor Cellular Nonlinear Networks Micromachines cellular nonlinear networks stochastic logic real time processing image processing memristors |
| title | Stochastic Computing Emulation of Memristor Cellular Nonlinear Networks |
| title_full | Stochastic Computing Emulation of Memristor Cellular Nonlinear Networks |
| title_fullStr | Stochastic Computing Emulation of Memristor Cellular Nonlinear Networks |
| title_full_unstemmed | Stochastic Computing Emulation of Memristor Cellular Nonlinear Networks |
| title_short | Stochastic Computing Emulation of Memristor Cellular Nonlinear Networks |
| title_sort | stochastic computing emulation of memristor cellular nonlinear networks |
| topic | cellular nonlinear networks stochastic logic real time processing image processing memristors |
| url | https://www.mdpi.com/2072-666X/13/1/67 |
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