Stochastic Computing Implementation of Chaotic Systems
An exploding demand for processing capabilities related to the emergence of the Internet of Things (IoT), Artificial Intelligence (AI), and big data, has led to the quest for increasingly efficient ways to expeditiously process the rapidly increasing amount of data. These ways include different appr...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-02-01
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| Series: | Mathematics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-7390/9/4/375 |
| _version_ | 1797396706127511552 |
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| author | Oscar Camps Stavros G. Stavrinides Rodrigo Picos |
| author_facet | Oscar Camps Stavros G. Stavrinides Rodrigo Picos |
| author_sort | Oscar Camps |
| collection | DOAJ |
| description | An exploding demand for processing capabilities related to the emergence of the Internet of Things (IoT), Artificial Intelligence (AI), and big data, has led to the quest for increasingly efficient ways to expeditiously process the rapidly increasing amount of data. These ways include different approaches like improved devices capable of going further in the more Moore path but also new devices and architectures capable of going beyond Moore and getting more than Moore. Among the solutions being proposed, Stochastic Computing has positioned itself as a very reasonable alternative for low-power, low-area, low-speed, and adjustable precision calculations—four key-points beneficial to edge computing. On the other hand, chaotic circuits and systems appear to be an attractive solution for (low-power, green) secure data transmission in the frame of edge computing and IoT in general. Classical implementations of this class of circuits require intensive and precise calculations. This paper discusses the use of the Stochastic Computing (SC) framework for the implementation of nonlinear systems, showing that it can provide results comparable to those of classical integration, with much simpler hardware, paving the way for relevant applications. |
| first_indexed | 2024-03-09T00:55:20Z |
| format | Article |
| id | doaj.art-2e058ce6fc674caaa4825b63e6fb91b1 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T00:55:20Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-2e058ce6fc674caaa4825b63e6fb91b12023-12-11T16:56:54ZengMDPI AGMathematics2227-73902021-02-019437510.3390/math9040375Stochastic Computing Implementation of Chaotic SystemsOscar Camps0Stavros G. Stavrinides1Rodrigo Picos2Industrial Engineering and Construction Department, University of Balearic Islands, 07122 Palma, SpainSchool of Science and Technology, International Hellenic University, 57001 Thessaloniki, GreeceIndustrial Engineering and Construction Department, University of Balearic Islands, 07122 Palma, SpainAn exploding demand for processing capabilities related to the emergence of the Internet of Things (IoT), Artificial Intelligence (AI), and big data, has led to the quest for increasingly efficient ways to expeditiously process the rapidly increasing amount of data. These ways include different approaches like improved devices capable of going further in the more Moore path but also new devices and architectures capable of going beyond Moore and getting more than Moore. Among the solutions being proposed, Stochastic Computing has positioned itself as a very reasonable alternative for low-power, low-area, low-speed, and adjustable precision calculations—four key-points beneficial to edge computing. On the other hand, chaotic circuits and systems appear to be an attractive solution for (low-power, green) secure data transmission in the frame of edge computing and IoT in general. Classical implementations of this class of circuits require intensive and precise calculations. This paper discusses the use of the Stochastic Computing (SC) framework for the implementation of nonlinear systems, showing that it can provide results comparable to those of classical integration, with much simpler hardware, paving the way for relevant applications.https://www.mdpi.com/2227-7390/9/4/375stochastic logicchaotic systemsapproximate computingshimizu-morioka systemchaotic circuitsfpga implementation |
| spellingShingle | Oscar Camps Stavros G. Stavrinides Rodrigo Picos Stochastic Computing Implementation of Chaotic Systems Mathematics stochastic logic chaotic systems approximate computing shimizu-morioka system chaotic circuits fpga implementation |
| title | Stochastic Computing Implementation of Chaotic Systems |
| title_full | Stochastic Computing Implementation of Chaotic Systems |
| title_fullStr | Stochastic Computing Implementation of Chaotic Systems |
| title_full_unstemmed | Stochastic Computing Implementation of Chaotic Systems |
| title_short | Stochastic Computing Implementation of Chaotic Systems |
| title_sort | stochastic computing implementation of chaotic systems |
| topic | stochastic logic chaotic systems approximate computing shimizu-morioka system chaotic circuits fpga implementation |
| url | https://www.mdpi.com/2227-7390/9/4/375 |
| work_keys_str_mv | AT oscarcamps stochasticcomputingimplementationofchaoticsystems AT stavrosgstavrinides stochasticcomputingimplementationofchaoticsystems AT rodrigopicos stochasticcomputingimplementationofchaoticsystems |