Cellular Automata Hardware Implementation

Sirakoulis, Georgios Ch.

doi:10.1007/978-1-4939-8700-9_673

Cited by 4 publications

(3 citation statements)

References 81 publications

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“…We have calibrated the Cell-DEVS models based on ground truth data collected from sensors installed in the corresponding physical system. 8 We hereby present our findings and extend the work by introducing a complete multistep framework proposal, as shown in Figure 1. The main contribution of this research and the presented framework is to provide a solution to two problems.…”

Section: Introductionsupporting

confidence: 58%

“…5,6 We have shown that Modeling and Simulation (M&S) can solve this dilemma by modeling the indoor spaces and validating the models by showing the resemblance between the models’ behavior and the real-life data. 7,8 Cell-DEVS is a combination of Cellular Automata (CA) and Discrete-Event systems Specifications that has several advantages described in section 3. Using the Cell-DEVS formalism, we have defined advanced 3-D models of real-life computer laboratories that consider CO 2 sources (i.e., occupants), CO 2 sinks (i.e., windows and ventilation ports), and the configuration of the closed space (e.g., dimensions).…”

Section: Introductionmentioning

confidence: 99%

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A framework for modeling, generating, simulating, and predicting carbon dioxide dispersion indoors using cell-DEVS and deep learning

Khalil,

Wainer

2023

SIMULATION

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Carbon dioxide concentration in enclosed spaces is an air quality indicator that affects occupants’ well-being. To maintain healthy carbon dioxide levels indoors, enclosed space settings must be adjusted to maximize air quality while minimizing energy consumption. Studying the effect of these settings on carbon dioxide concentration levels is not feasible through physical experimentation and data collection. This problem can be solved by using validated simulation models, generating indoor settings scenarios, simulating those scenarios, and studying results. In previous work, we presented a formal Cellular Discrete Event System Specifications simulation model for studying carbon dioxide dispersion in rooms with various settings. However, designers may need to predict the results of altering large combinations of settings on air quality. Generating and simulating multiple scenarios with different combinations of space settings to test their effect on indoor air quality is time-consuming. In this research, we solve the two problems of the lack of ground truth data and the inefficiency of producing and studying simulation results for many combinations of settings by proposing a novel framework. The framework utilizes a Cellular Discrete Event System Specifications model, simulates different scenarios of enclosed spaces with various settings, and collects simulation results to form a data set to train a deep neural network. Without needing to generate all possible scenarios, the trained deep neural network is used to predict unknown settings of the closed space when other settings are altered. The framework facilitates configuring enclosed spaces to enhance air quality. We illustrate the framework uses through a case study.

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Section: Introductionsupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

A framework for modeling, generating, simulating, and predicting carbon dioxide dispersion indoors using cell-DEVS and deep learning

Khalil,

Wainer

2023

SIMULATION

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“…CA modeling of chemical processes [8], for example, has been found to improve the understanding of chemical systems such as the Belousov-Zhabotinsky reaction [9], [10], [11], [12]. Furthermore, their applications in biological system modeling range from excitable and oscillating mediums that mimic brain events like nerve cell excitation to DNA sequencing [13], [14]. CA's potential for simulating biological systems such as plant succession and wildfire spread has also been examined [15], [16].…”

Section: Introductionmentioning

confidence: 99%

MemCA: All-Memristor Design for Deterministic and Probabilistic Cellular Automata Hardware Realization

et al. 2023

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Inspired by the behavior of natural systems, Cellular Automata (CA) tackle the demanding long-distance information transfer of conventional computers by the massive parallel computation performed by a set of locally-coupled dynamical nodes. Although CA are envisioned as powerful deterministic computers, their intrinsic capabilities are expanded after the memristor's probabilistic switching is introduced into CA cells, resulting in new hybrid deterministic and probabilistic memristor-based CA (MemCA). In the proposed MemCA hardware realization, memristor devices are incorporated in both the cell and rule modules, composing the very first all-memristor CA hardware, designed with mixed CMOS/Memristor circuits. The proposed implementation accomplishes high operating speed and reduced area requirements, exploiting also memristor as an entropy source in every CA cell. MemCA's functioning is showcased in deterministic and probabilistic operation, which can be externally modified by the selection of programming voltage amplitude, without changing the design. Also, the proposed MemCA system includes a reconfigurable rule module implementation that allows for spatial and temporal rule inhomogeneity.

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