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Hybrid Pipeline Structure for Self-Organizing Learning Array ISNN 2007: The 4th International Symposium on Neural Networks Yinyin Liu1, Ding Mingwei2 , Janusz A. Starzyk1, 1 School of Electrical Engineering & Computer Science Ohio University, USA 2 Ross University Outline • RC systems design of SOLAR • Dimensionality reduction • Input selection, weighting • Pipeline structure Motor cortex Pars opercularis Sensory associative cortex Visual associative cortex • Experimental results • Conclusions Somatosensory cortex Broca’s area Visual cortex Primary Auditory cortex Wernicke’s area 2 Intelligence AI’s holy grail From Pattie Maes MIT Media Lab • “…Perhaps the last frontier of science – its ultimate challenge- is to understand the biological basis of consciousness and the mental process by which we perceive, act, learn and remember..” from Principles of Neural Science by E. R. Kandel et al. E. R. Kandel won Nobel Price in 2000 for his work on physiological basis of memory storage in neurons. • “…The question of intelligence is the last great terrestrial frontier of science...” from Jeff Hawkins On Intelligence. Jeff Hawkins founded the Redwood Neuroscience Institute devoted to brain research. He co-founded Palm Computing and Handspring Inc. 3 How can we design intelligence? • We need to know how • We need means to implement it • We need resources to build and sustain its operation 4 Resources – Evolution of Electronics From Ray Kurzwail, The Singularity Summit at Stanford, May 13, 20065 By Gordon E. Moore 6 7 Clock Speed (doubles every 2.7 years) From Ray Kurzwail, The Singularity Summit at Stanford, May 13, 20068 From Ray Kurzwail, The Singularity Summit at Stanford, May 13, 2006 9 Outline • RC systems design of SOLAR • Dimensionality reduction • Input selection, weighting • Pipeline structure Motor cortex Pars opercularis • Experimental results • Conclusions Somatosensory cortex Sensory associative cortex Visual associative cortex Broca’s area Visual cortex Primary Auditory cortex Wernicke’s area 10 Traditional ANN Hardware information flow – Limited routing resource. output – Quadratic relationship between the routing and the number of neuron makes classical ANNs wire dominated. Interconnect is 70% of chip area input hidden 11 Biological Neural Networks Cell body From IFC’s webpage Dowling, 1998, p. 17 12 Sparse Structure • 1012 neurons in human brain are sparsely connected • On average, each neuron is connected to other neurons through about 104 synapses • Sparse structure enables efficient computation and saves energy and cost 13 Why should we care? Source: SEMATECH 14 Design Productivity Gap Low-Value Designs? Percent of die area that must be occupied by memory to maintain SOC design productivity 100% 80% 60% % Area Memory 40% % Area Reused Logic 20% % Area New Logic 19 99 20 02 20 05 20 08 20 11 20 14 0% Source = Japanese system-LSI industry 15 Outline • RC systems design of SOLAR • Dimensionality reduction • Input selection, weighting • Pipeline structure • Experimental results Motor cortex Pars opercularis • Conclusions Somatosensory cortex Sensory associative cortex Visual associative cortex Broca’s area Visual cortex Primary Auditory cortex Wernicke’s area 16 SOLAR System Design • SOLAR Introduction – – Entropy based selforganization data-driven Local connection Dynamical reconfiguration Local and sparse interconnections Online inputs selection Feature neurons and merging neurons Pattern recognition, classification 17 Pipeline Overview node computing ability → “soft” connections Four modes 1. Idle 2. Read 3. Process 4. Write 18 Pipeline Signal Flow 1 19 Pipeline Signal Flow 2 20 Pipeline Signal Flow 3 21 Node Operations Implemented with Xilinx picoBlaze Runs at higher frequency 22 Outline • RC systems design of SOLAR • Dimensionality reduction • Input selection, weighting • Pipeline structure Motor cortex Pars opercularis • Experimental results • Conclusions Somatosensory cortex Sensory associative cortex Visual associative cortex Broca’s area Visual cortex Primary Auditory cortex Wernicke’s area 23 Em(x) Simulation Results 24 Iris Data Processing Linear growth of HW cost 4x7 array processing Iris data 25 Chip Layout 26 Hardware Development 27 Future Work - System SOLAR 28 Conclusions & Future work • Sparse coding building in sparsely connected networks • WTA scheme: local competition accomplish the global competition using primary and secondary layers –efficient hardware implementation • OTA scheme: local competition produces neuronal activity reduction • OTA – redundant coding: more reliable and robust • WTA & OTA: learning memory for developing machine intelligence Future work: • Introducing temporal sequence learning • Building motor pathway on such learning memory • Combining with goal-creation pathway to build intelligent machine 29