An Overview of Hardware Implementation of Membrane Computing Models

Zhang, Gexiang; Shang, Zeyi; Verlan, Sergey; Amor, Miguel Ángel Martínez del; Yuan, Chengxun; Valencia-Cabrera, Luis; Pérez–Jiménez, Mario J.

doi:10.1145/3402456

Cited by 25 publications

(8 citation statements)

References 68 publications

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“…In [17], the authors performed an overview of the approaches of hardware implementation in the area of P systems. They compared and evaluated quantitative and qualitative attributes of FPGA-based implementations and CUDA-enabled GPU-based simulations.…”

Section: Membrane Systemsmentioning

confidence: 99%

P System–Based Clustering Methods Using NoSQL Databases

2021

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Models of computation are fundamental notions in computer science; consequently, they have been the subject of countless research papers, with numerous novel models proposed even in recent years. Amongst a multitude of different approaches, many of these methods draw inspiration from the biological processes observed in nature. P systems, or membrane systems, make an analogy between the communication in computing and the flow of information that can be perceived in living organisms. These systems serve as a basis for various concepts, ranging from the fields of computational economics and robotics to the techniques of data clustering. In this paper, such utilization of these systems—membrane system–based clustering—is taken into focus. Considering the growing number of data stored worldwide, more and more data have to be handled by clustering algorithms too. To solve this issue, bringing these methods closer to the data, their main element provides several benefits. Database systems equip their users with, for instance, well-integrated security features and more direct control over the data itself. Our goal is if the type of the database management system is given, e.g., NoSQL, but the corporation or the research team can choose which specific database management system is used, then we give a perspective, how the algorithms written like this behave in such an environment, so that, based on this, a more substantiated decision can be made, meaning which database management system should be connected to the system. For this purpose, we discover the possibilities of a clustering algorithm based on P systems when used alongside NoSQL database systems, that are designed to manage big data. Variants over two competing databases, MongoDB and Redis, are evaluated and compared to identify the advantages and limitations of using such a solution in these systems.

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Section: Membrane Systemsmentioning

confidence: 99%

P System–Based Clustering Methods Using NoSQL Databases

2021

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“…For an NSNP-DW system with at most m neurons and at most 2 rules in each neuron, we use N gen SNP 2 m to represent all natural number sets generated by the NSNP-DW system and N acc SNP 2 m to represent all natural number sets accepted by the NSNP-DW system. When the number of neurons is uncertain, m is often replaced by * .…”

Section: Definitionmentioning

confidence: 99%

“…It was proposed by Gheorghe Păun in 1998 and published in 2000 [ 1 ]. As a new type of natural computing, membrane computing has abundant model support [ 2 – 4 ] and is widely used in real life [ 5 , 6 ]. The distributed computing model is named after the membrane system or P system.…”

Section: Introductionmentioning

confidence: 99%

Universal Nonlinear Spiking Neural P Systems with Delays and Weights on Synapses

Wang

Liu

Zhao

2021

Computational Intelligence and Neuroscience

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The nonlinear spiking neural P systems (NSNP systems) are new types of computation models, in which the state of neurons is represented by real numbers, and nonlinear spiking rules handle the neuron’s firing. In this work, in order to improve computing performance, the weights and delays are introduced to the NSNP system, and universal nonlinear spiking neural P systems with delays and weights on synapses (NSNP-DW) are proposed. Weights are treated as multiplicative constants by which the number of spikes is increased when transiting across synapses, and delays take into account the speed at which the synapses between neurons transmit information. As a distributed parallel computing model, the Turing universality of the NSNP-DW system as number generating and accepting devices is proven. 47 and 43 neurons are sufficient for constructing two small universal NSNP-DW systems. The NSNP-DW system solving the Subset Sum problem is also presented in this work.

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“…The increasing need of efficient simulators for P systems, motivated by the requirements of both real-life applications and theoretical research, has led to a new research line within membrane computing focusing on implementing real parallelism on high-performance computing platforms [33]: FPGAs 1 , GPUs, computer networks, big data environments, etc. In this section, we will focus on the implementation of P system parallelism on graphics processing units (GPUs), which turns out to be one of the most explored technologies for this purpose [22].…”

Section: Parallel Implementationsmentioning

confidence: 99%

Simulation challenges in membrane computing

Valencia-Cabrera¹,

Pérez–Hurtado²,

Amor³

2020

J Membr Comput

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P system simulators are critical tools to enable them as formal modeling framework for real-life applications. Such simulators abstract the concept of P systems in various ways, depending on the needs of the users and the requirements of the specific application. We identify three main levels of abstraction: graphical user interfaces, simulation engines and parallel implementations. In this paper, we survey the state of the art at these levels and discuss the main challenges under consideration for future developments.

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An Overview of Hardware Implementation of Membrane Computing Models

Cited by 25 publications

References 68 publications

P System–Based Clustering Methods Using NoSQL Databases

P System–Based Clustering Methods Using NoSQL Databases

Universal Nonlinear Spiking Neural P Systems with Delays and Weights on Synapses

Simulation challenges in membrane computing

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