2026-06-14T03:53:08Zhttps://repisalud.isciii.es/rest/oai/request

oai:repisalud.isciii.es:20.500.12105/233042024-11-28T23:10:03Zcom_20.500.12105_15322com_20.500.12105_2051col_20.500.12105_16967

00925njm 22002777a 4500 dc Camps, Oscar author Stavrinides, Stavros G author Picos, Rodrigo author 2021-02 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. Camps O, Stavrinides SG, Picos R. Stochastic Computing Implementation of Chaotic Systems. Mathematics. 2021 Feb;9(4):375. 10.3390/math9040375 2227-7390 Mathematics https://hdl.handle.net/20.500.13003/19832 2-s2.0-85101259391 https://hdl.handle.net/20.500.12105/23304 624155100001 Stochastic logic Chaotic systems Approximate computing Shimizu-morioka system Chaotic circuits fpga implementation Stochastic Computing Implementation of Chaotic Systems