Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Stochastic Computing Implementation of Chaotic Systems

Version 1 : Received: 8 January 2021 / Approved: 11 January 2021 / Online: 11 January 2021 (14:56:15 CET)

A peer-reviewed article of this Preprint also exists.

Camps, O.; Stavrinides, S.G.; Picos, R. Stochastic Computing Implementation of Chaotic Systems. Mathematics 2021, 9, 375. Camps, O.; Stavrinides, S.G.; Picos, R. Stochastic Computing Implementation of Chaotic Systems. Mathematics 2021, 9, 375.

Journal reference: Mathematics 2021, 9, 375
DOI: 10.3390/math9040375

Abstract

An exploding demand for processing capabilities related to the emergence of the IoT, 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 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.

Subject Areas

Stochastic Logic; Chaotic Systems; Approximate Computing; Shimizu-Morioka System; Chaotic Circuits; FPGA Implementation

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