Parallelizing two classes of neuromorphic models on the Cell multicore architecture

There is a significant interest in the research community to develop large scale, high performance implementations of cortical models. These have the potential to provide significantly stronger information processing capabilities than current computing algorithms. At present we are investigating the implementation of six neuromorphic computational models on a large Sony PlayStation 3 cluster at the Air Force research lab. These six models span two classes of neuromorphic algorithms: hierarchical Bayesian and spiking neural networks. In this paper we have presented the performance gain of these six neuromorphic computational models for implementations on the Cell multicore processor on the PlayStation 3. We show that the Cell multicore architecture can provide significant performance gains for these models. We compare the performance gains of the two classes and see that the hierarchical Bayesian class provides higher speedups than the spiking network class in general. This is primarily due to the higher computational load per node in the former class. Our results indicate that the Cell processor based PlayStation 3 would provide a good platform for the large scale implementation of the classes of neuromorphic models examined.

[1]  D. George,et al.  A hierarchical Bayesian model of invariant pattern recognition in the visual cortex , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[2]  Lyle N. Long,et al.  Character Recognition using Spiking Neural Networks , 2007, 2007 International Joint Conference on Neural Networks.

[3]  Anders Lansner,et al.  Non-commercial Research and Educational Use including without Limitation Use in Instruction at Your Institution, Sending It to Specific Colleagues That You Know, and Providing a Copy to Your Institution's Administrator. All Other Uses, Reproduction and Distribution, including without Limitation Comm , 2022 .

[4]  Jack J. Dongarra,et al.  The PlayStation 3 for High-Performance Scientific Computing , 2008, Computing in Science & Engineering.

[5]  Tai Sing Lee,et al.  Hierarchical Bayesian inference in the visual cortex. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[6]  C. Morris,et al.  Voltage oscillations in the barnacle giant muscle fiber. , 1981, Biophysical journal.

[7]  Thomas L. Dean,et al.  Hierarchical Expectation Refinement for Learning Generative Perception Models , 2005 .

[8]  Thomas Dean,et al.  A Computational Model of the Cerebral Cortex , 2005, AAAI.

[9]  Thomas Dean,et al.  Learning invariant features using inertial priors , 2006, Annals of Mathematics and Artificial Intelligence.

[10]  Dileep George,et al.  How the brain might work: a hierarchical and temporal model for learning and recognition , 2008 .

[11]  Kevin Murphy,et al.  Bayes net toolbox for Matlab , 1999 .

[12]  A. Hodgkin,et al.  A quantitative description of membrane current and its application to conduction and excitation in nerve , 1952, The Journal of physiology.

[13]  Martin Hopkins,et al.  Synergistic Processing in Cell's Multicore Architecture , 2006, IEEE Micro.

[14]  R. Linderman.  Early Experiences with Algorithm Optimizations on Clusters of Playstation 3's , 2008, 2008 DoD HPCMP Users Group Conference.

[15]  Y. Dan,et al.  Spike Timing-Dependent Plasticity of Neural Circuits , 2004, Neuron.

[16]  Dharmendra S. Modha,et al.  Anatomy of a cortical simulator , 2007, Proceedings of the 2007 ACM/IEEE Conference on Supercomputing (SC '07).

[17]  D. George,et al.  Hierarchical Temporal Memory Concepts , Theory , and Terminology , 2006 .

[18]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems - networks of plausible inference , 1991, Morgan Kaufmann series in representation and reasoning.

[19]  H. Wilson.  Simplified dynamics of human and mammalian neocortical neurons. , 1999, Journal of theoretical biology.

[20]  Jack Dongarra,et al.  Limitations of the PlayStation 3 for High Performance Cluster Computing , 2007 .

[21]  David J. Spiegelhalter,et al.  Local computations with probabilities on graphical structures and their application to expert systems , 1990 .

[22]  Stamatis Vassiliadis,et al.  Block Based Compression Storage Expected Performance , 2002 .

[23]  Eugene M. Izhikevich,et al.  Simple model of spiking neurons , 2003, IEEE Trans. Neural Networks.

[24]  Glenn Carroll,et al.  On the Prospects for Building a Working Model of the Visual Cortex , 2007, AAAI.

[25]  Qing Wu,et al.  Performance optimization for pattern recognition using associative neural memory , 2008, 2008 IEEE International Conference on Multimedia and Expo.

[26]  Judea Pearl,et al.  Probabilistic reasoning in intelligent systems , 1988 .

[27]  H. Markram.  The Blue Brain Project , 2006, Nature Reviews Neuroscience.

[28]  Vernon B Mountcastle,et al.  Introduction. Computation in cortical columns. , 2003, Cerebral cortex.

[29]  J. Hawkins,et al.  On Intelligence , 2004 .

[30]  Eugene M. Izhikevich,et al.  Which model to use for cortical spiking neurons? , 2004, IEEE Transactions on Neural Networks.

[31]  Thomas L. Dean,et al.  Scalable Inference in Hierarchical Generative Models , 2006, AI&M.

[32]  Simon J Thorpe,et al.  SpikeNET: an event-driven simulation package for modelling large networks of spiking neurons , 2003, Network.