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University of Manchester

1. Jin, Xin. Parallel simulation of neural networks on SpiNNaker universal neuromorphic hardware.

Degree: PhD, 2010, University of Manchester

Artificial neural networks have shown great potential and have attracted much research interest. One problem faced when simulating such networks is speed. As the number of neurons increases, the time to simulate and train a network increases dramatically. This makes it difficult to simulate and train a large-scale network system without the support of a high-performance computer system. The solution we present is a "real" parallel system - using a parallel machine to simulate neural networks which are intrinsically parallel applications. SpiNNaker is a scalable massively-parallel computing system under development with the aim of building a general-purpose platform for the parallel simulation of large-scale neural systems. This research investigates how to model large-scale neural networks efficiently on such a parallel machine. While providing increased overall computational power, a parallel architecture introduces a new problem - the increased communication reduces the speedup gains. Modeling schemes, which take into account communication, processing, and storage requirements, are investigated to solve this problem. Since modeling schemes are application-dependent, two different types of neural network are examined - spiking neural networks with spike-time dependent plasticity, and the parallel distributed processing model with the backpropagation learning rule. Different modeling schemes are developed and evaluated for the two types of neural network. The research shows the feasibility of the approach as well as the performance of SpiNNaker as a general-purpose platform for the simulation of neural networks. The linear scalability shown in this architecture provides a path to the further development of parallel solutions for the simulation of extremely large-scale neural networks.

Subjects/Keywords: 004.01; SpiNNkaer; Spiking neural network; Parallel simulation; Izhikevich; ARM; Real-time; PDP; MLP; Backpropagation; STDP

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Jin, X. (2010). Parallel simulation of neural networks on SpiNNaker universal neuromorphic hardware. (Doctoral Dissertation). University of Manchester. Retrieved from https://www.research.manchester.ac.uk/portal/en/theses/parallel-simulation-of-neural-networks-on-spinnaker-universal-neuromorphic-hardware(d6b8b72a-63c4-44ee-963a-ae349b0e379c).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518472

Chicago Manual of Style (16th Edition):

Jin, Xin. “Parallel simulation of neural networks on SpiNNaker universal neuromorphic hardware.” 2010. Doctoral Dissertation, University of Manchester. Accessed December 09, 2019. https://www.research.manchester.ac.uk/portal/en/theses/parallel-simulation-of-neural-networks-on-spinnaker-universal-neuromorphic-hardware(d6b8b72a-63c4-44ee-963a-ae349b0e379c).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518472.

MLA Handbook (7th Edition):

Jin, Xin. “Parallel simulation of neural networks on SpiNNaker universal neuromorphic hardware.” 2010. Web. 09 Dec 2019.

Vancouver:

Jin X. Parallel simulation of neural networks on SpiNNaker universal neuromorphic hardware. [Internet] [Doctoral dissertation]. University of Manchester; 2010. [cited 2019 Dec 09]. Available from: https://www.research.manchester.ac.uk/portal/en/theses/parallel-simulation-of-neural-networks-on-spinnaker-universal-neuromorphic-hardware(d6b8b72a-63c4-44ee-963a-ae349b0e379c).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518472.

Council of Science Editors:

Jin X. Parallel simulation of neural networks on SpiNNaker universal neuromorphic hardware. [Doctoral Dissertation]. University of Manchester; 2010. Available from: https://www.research.manchester.ac.uk/portal/en/theses/parallel-simulation-of-neural-networks-on-spinnaker-universal-neuromorphic-hardware(d6b8b72a-63c4-44ee-963a-ae349b0e379c).html ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.518472


University of Manchester

2. Jin, Xin. Parallel Simulation of Neural Networks on SpiNNaker Universal Neuromorphic Hardware.

Degree: 2010, University of Manchester

Artificial neural networks have shown great potential and have attracted much research interest. One problem faced when simulating such networks is speed. As the number of neurons increases, the time to simulate and train a network increases dramatically. This makes it difficult to simulate and train a large-scale network system without the support of a high-performance computer system. The solution we present is a ``real'' parallel system  – using a parallel machine to simulate neural networks which are intrinsically parallel applications.SpiNNaker is a scalable massively-parallel computing system under development with the aim of building a general-purpose platform for the parallel simulation of large-scale neural systems. This research investigates how to model large-scale neural networks efficiently on such a parallel machine. While providing increased overall computational power, a parallel architecture introduces a new problem  – the increased communication reduces the speedup gains. Modeling schemes, which take into account communication, processing, and storage requirements, are investigated to solve this problem. Since modeling schemes are application-dependent, two different types of neural network are examined  – spiking neural networks with spike-time dependent plasticity, and the parallel distributed processing model with the backpropagation learning rule. Different modeling schemes are developed and evaluated for the two types of neural network. The research shows the feasibility of the approach as well as the performance of SpiNNaker as a general-purpose platform for the simulation of neural networks. The linear scalability shown in this architecture provides a path to the further development of parallel solutions for the simulation of extremely large-scale neural networks. Advisors/Committee Members: Furber, Stephen.

Subjects/Keywords: SpiNNkaer; Spiking neural network; Parallel simulation; Izhikevich; ARM; Real-time; PDP; MLP; Backpropagation; STDP

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Jin, X. (2010). Parallel Simulation of Neural Networks on SpiNNaker Universal Neuromorphic Hardware. (Doctoral Dissertation). University of Manchester. Retrieved from http://www.manchester.ac.uk/escholar/uk-ac-man-scw:85336

Chicago Manual of Style (16th Edition):

Jin, Xin. “Parallel Simulation of Neural Networks on SpiNNaker Universal Neuromorphic Hardware.” 2010. Doctoral Dissertation, University of Manchester. Accessed December 09, 2019. http://www.manchester.ac.uk/escholar/uk-ac-man-scw:85336.

MLA Handbook (7th Edition):

Jin, Xin. “Parallel Simulation of Neural Networks on SpiNNaker Universal Neuromorphic Hardware.” 2010. Web. 09 Dec 2019.

Vancouver:

Jin X. Parallel Simulation of Neural Networks on SpiNNaker Universal Neuromorphic Hardware. [Internet] [Doctoral dissertation]. University of Manchester; 2010. [cited 2019 Dec 09]. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:85336.

Council of Science Editors:

Jin X. Parallel Simulation of Neural Networks on SpiNNaker Universal Neuromorphic Hardware. [Doctoral Dissertation]. University of Manchester; 2010. Available from: http://www.manchester.ac.uk/escholar/uk-ac-man-scw:85336

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