The Stochastic Loss of Spikes in Spiking Neural P Systems : Design and Implementation of Reliable Arithmetic Circuits

• Autorzy: Xu Z. , Cavaliere M. , An P. , Vrudhula S. , Cao Y

Identyfikatory

DOI

10.3233/FI-2014-1098

• Języki publikacji: EN

Abstrakty

EN

Spiking neural P systems (in short, SN P systems) have been introduced as computing devices inspired by the structure and functioning of neural cells. The presence of unreliable components in SN P systems can be considered in many different aspects. In this paper we focus on two types of unreliability: the stochastic delays of the spiking rules and the stochastic loss of spikes. We propose the implementation of elementary SN P systems with DRAM-based CMOS circuits that are able to cope with these two forms of unreliability in an efficient way. The constructed bio-inspired circuits can be used to encode basic arithmetic modules.

Słowa kluczowe

EN

neurons; stochastic systems; complementary metal oxide semiconductors; neural networks; biologically inspired computing

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2014
• Tom: Vol. 134, nr 1/2
• Strony: 183--200
• Opis fizyczny: Bibliogr. 16 poz., rys., tab., wykr.

Twórcy

autor

Xu Z.

• School of Electrical Computer and Energy Engineering, Arizona State University, USA

autor

Cavaliere M.

• School of Informatics, University of Edinburgh, UK

autor

An P.

• School of Electrical Computer and Energy Engineering, Arizona State University, USA

autor

Vrudhula S.

• School of Electrical Computer and Energy Engineering, Arizona State University, USA

autor

Cao Y

• School of Electrical Computer and Energy Engineering, Arizona State University, USA

Bibliografia

• [1] Bernstein K., Cavin R. K., Porod W., Seabaugh A., Welser J. Device and Architecture Outlook for Beyond CMOS Switches. Proc. IEEE, 98, 12, 2010.
• [2] Cavaliere M., Mura I. Experiments on the Reliability of Stochastic Spiking Neural P Systems. Natural Computing 7, 4, 2008.
• [3] Cavaliere M., Sburlan D. Time-independent P systems. In Membrane Computing. International Workshop WMC5, Milano, Italy, 2004, LNCS 3365, Springer, 2005, pp. 239–258.
• [4] Gerstner W.. Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking. Neural Computation, 12, 43, 2000.
• [5] Guitérrez-Naranjo M. A., Leporati A. First Steps towards a CPU made of Spiking Neural P Systems. J. Comput. Commun. Control, 5, 3, 2009.
• [6] Indiveri G. et al. Neuromoprhic Silicon Neuron Circuits. Frontiers in Neuroscience, 5, 73, 2011.
• [7] Păun Gh. Spiking Neural P Systems: A Tutorial. Bulletin of the EATCS, 91 (Feb 2007).
• [8] Păun Gh., Rozenberg G., Salomaa A. Eds. The Oxford Handbook of Membrane Computing. Oxford University Press, 2010.
• [9] Knight B.W. Dynamics of Encoding in a Population of Neurons. J. Gen. Physiol., 59, 6, 1972.
• [10] Kuzum D., Jeyasingh R.G.D., Lee B., Wong H.-S.P. Nanoelectronic Programmable Synapses based on Phase Change Materials for Brain-Inspired Computing. Nano Lett, 12, 5, 2011.
• [11] Schmitt M. On Computing Boolean Functions by a Spiking Neuron. J. Annals of Mathematics and Artificial Intelligence, 24, 1-4, 1998.
• [12] Sharad M., Augustine C., Panagopoulos G., Roy K. Spin-Based Neuron Model with Domain-Wall Magnets as Synapse. Trans. Nanotechnology, 11, 4, 2012.
• [13] Song T., Maciás-Ramos L.F., Pan L., Pérez-Jiménez M.J. Time-Free Solution to SAT Problem Using P Systems with Active Membranes. Theoretical Computer Science, 529, 2014.
• [14] Pan L., Zeng X., Zhang X. Time-Free Spiking Neural P Systems. Neural Computation, 23, 5, 2011.
• [15] Zeng X., Song T., Zhang X., Pan L. Performing four Basic Arithmetic Operations with Spiking Neural P Systems. Trans. NanoBioscience, 11, 4, 2012.
• [16] Predictive Technology Model, available at http://ptm.asu.edu