Simulation of Spiking Neural P Systems with Sparse Matrix-Vector Operations

To date, parallel simulation algorithms for spiking neural P (SNP) systems are based on a matrix representation. This way, the simulation is implemented with linear algebra operations, which can be easily parallelized on high performance computing platforms such as GPUs. Although it has been convenient for the first generation of GPU-based simulators, such as CuSNP, there are some bottlenecks to sort out. For example, the proposed matrix representations of SNP systems lead to very sparse matrices, where the majority of values are zero. It is known that sparse matrices can compromise the performance of algorithms since they involve a waste of memory and time. This problem has been extensively studied in the literature of parallel computing. In this paper, we analyze some of these ideas and apply them to represent some variants of SNP systems. We also provide a new simulation algorithm based on a novel compressed representation for sparse matrices. We also conclude which SNP system variant better suits our new compressed matrix representation.

[1]  Xiaohui Luo,et al.  Dendrite P systems , 2020, Neural Networks.

[2]  Ravie Chandren Muniyandi,et al.  A Representation of Membrane Computing with a Clustering Algorithm on the Graphical Processing Unit , 2020, Processes.

[3]  Agustín Riscos-Núñez,et al.  Dendrite P Systems Toolbox: Representation, Algorithms and Simulators. , 2020, International journal of neural systems.

[4]  Mihai Ionescu,et al.  Some Applications of Spiking Neural P Systems , 2008, Comput. Informatics.

[5]  Luis Fernando de Mingo López,et al.  An In Vivo Proposal of Cell Computing Inspired by Membrane Computing , 2021, Processes.

[6]  Kay Chen Tan,et al.  Numerical Spiking Neural P Systems. , 2020, IEEE transactions on neural networks and learning systems.

[7]  Gheorghe Paun,et al.  Computing with Membranes , 2000, J. Comput. Syst. Sci..

[8]  Francis George Cabarle,et al.  Spiking Neural P Systems with Structural Plasticity: Attacking the Subset Sum Problem , 2015, Int. Conf. on Membrane Computing.

[9]  Henry N. Adorna,et al.  Spiking neural P systems with structural plasticity , 2015, Neural Computing and Applications.

[10]  Linqiang Pan,et al.  Spiking Neural P Systems: Theoretical Results and Applications , 2018, Enjoying Natural Computing.

[11]  Ivan Cedric H. Macababayao,et al.  Snapse: A Visual Tool for Spiking Neural P Systems , 2020, Processes.

[12]  Haina Rong,et al.  On Applications of Spiking Neural P Systems , 2020 .

[13]  Cesare Alippi,et al.  Data-based fault tolerant control for affine nonlinear systems through particle swarm optimized neural networks , 2020, IEEE/CAA Journal of Automatica Sinica.

[14]  Kenli Li,et al.  A Survey of Nature-Inspired Computing , 2021, ACM Comput. Surv..

[15]  Fan Yang,et al.  Spiking neural P systems with multiple channels and anti-spikes , 2018, Biosyst..

[16]  Giancarlo Mauri,et al.  Uniform solutions to SAT and Subset Sum by spiking neural P systems , 2008, Natural Computing.

[17]  Xiangxiang Zeng,et al.  Spiking Neural P Systems With Scheduled Synapses , 2017, IEEE Transactions on NanoBioscience.

[18]  M. A. Martínez-del-Amor,et al.  CuSNP : Spiking Neural P Systems Simulators in CUDA Jym , 2017 .

[19]  Richard George,et al.  Structural Plasticity Denoises Responses and Improves Learning Speed , 2016, Front. Comput. Neurosci..

[20]  Henry N. Adorna,et al.  Improving GPU Simulations of Spiking Neural P Systems , 2012 .

[21]  Minghe Sun,et al.  Novel Numerical Spiking Neural P Systems with a Variable Consumption Strategy , 2021 .

[22]  Linqiang Pan,et al.  Spiking Neural P Systems With Rules on Synapses Working in Maximum Spiking Strategy , 2014, IEEE Transactions on NanoBioscience.

[23]  MengChu Zhou,et al.  Randomized latent factor model for high-dimensional and sparse matrices from industrial applications , 2019, IEEE CAA J. Autom. Sinica.

[24]  Henry N. Adorna,et al.  A return to stochasticity and probability in spiking neural P systems , 2021, J. Membr. Comput..

[25]  Pan Linqiang,et al.  Spiking neural P systems with neuron division and budding , 2011 .

[26]  Haina Rong,et al.  A Novel Spiking Neural P System for Image Recognition , 2021, Int. J. Unconv. Comput..

[27]  Wei Liu,et al.  Optimal Siting and Sizing of Distributed Generation Based on Improved Nondominated Sorting Genetic Algorithm II , 2019 .

[28]  Oscar H. Ibarra,et al.  On spiking neural P systems , 2006, Natural Computing.

[29]  Mario J. Pérez-Jiménez,et al.  P-Lingua in two steps: flexibility and efficiency , 2019, J. Membr. Comput..

[30]  Gheorghe Paun,et al.  Spiking Neural P Systems. Recent Results, Research Topics , 2009, Algorithmic Bioprocesses.

[31]  Henry N. Adorna,et al.  Homogeneous spiking neural P systems with structural plasticity , 2021, J. Membr. Comput..

[32]  Henry N. Adorna,et al.  optimizations in CuSNP Simulator for Spiking Neural P Systems on CUDA GPUs , 2019, 2019 International Conference on High Performance Computing & Simulation (HPCS).

[33]  Mario J. Pérez-Jiménez,et al.  A P-Lingua Based Simulator for Spiking Neural P Systems , 2011, Int. Conf. on Membrane Computing.

[34]  Pat Hanrahan,et al.  Understanding the efficiency of GPU algorithms for matrix-matrix multiplication , 2004, Graphics Hardware.

[35]  Pei Hu,et al.  Novel Parallel Heterogeneous Meta-Heuristic and Its Communication Strategies for the Prediction of Wind Power , 2019, Processes.

[36]  Najib Essounbouli,et al.  Neural network based adaptive tracking control for a class of pure feedback nonlinear systems with input saturation , 2019, IEEE/CAA Journal of Automatica Sinica.

[37]  Henry N. Adorna,et al.  Handling Non-determinism in Spiking Neural P Systems: Algorithms and Simulations , 2019, Fundam. Informaticae.

[38]  Linqiang Pan,et al.  Spiking Neural P Systems with Astrocytes , 2012, Neural Computation.

[39]  Otgonnaran Ochirbat,et al.  An error-tolerant serial binary full-adder via a spiking neural P system using HP/LP basic neurons , 2020, J. Membr. Comput..

[40]  Mario J. Pérez-Jiménez,et al.  Simulating P Systems on GPU Devices: A Survey , 2015, Fundam. Informaticae.

[41]  Gheorghe Păun,et al.  Spiking Neural P Systems with Weights , 2010, Neural Computation.

[42]  Jiujun Cheng,et al.  Dendritic Neuron Model With Effective Learning Algorithms for Classification, Approximation, and Prediction , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[43]  Aiiad Albeshri,et al.  Performance Analysis of Sparse Matrix-Vector Multiplication (SpMV) on Graphics Processing Units (GPUs) , 2020 .

[44]  Xiangrong Liu,et al.  On solutions and representations of spiking neural P systems with rules on synapses , 2019, Inf. Sci..

[45]  Xiangxiang Zeng,et al.  Matrix Representation of Spiking Neural P Systems , 2010, Int. Conf. on Membrane Computing.

[46]  Xiangxiang Zeng,et al.  Matrix representation and simulation algorithm of spiking neural P systems with structural plasticity , 2019, J. Membr. Comput..

[47]  Henry N. Adorna,et al.  A Framework for Evolving Spiking Neural P Systems , 2021, Int. J. Unconv. Comput..

[48]  Mario J. Pérez-Jiménez,et al.  Adaptative parallel simulators for bioinspired computing models , 2020, Future Gener. Comput. Syst..