Neurmorphic Architectures Historical Highlights Analog VLSI Artificial Neural Network Chips SYNAPSE-1 System Architecture

Neurmorphic Architectures Historical Highlights Analog VLSI Artificial Neural Network Chips SYNAPSE-1 System Architecture www.phwiki.com

Neurmorphic Architectures Historical Highlights Analog VLSI Artificial Neural Network Chips SYNAPSE-1 System Architecture

Cranley, Nora, Fashion Editor has reference to this Academic Journal, PHwiki organized this Journal Neurmorphic Architectures Kenneth Rice in addition to Tarek Taha Clemson University Historical Highlights Analog VLSI Carver Mead in addition to his students pioneered the development aVLSI technology as long as use in neural circuits They developed a silicon retina which electronically emulated the first 3 layers of the retina Image from [3]

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Artificial Neural Network Chips Early neuromorphic architectures were artificial neural network chips Examples: ETANN : (1989) Entirely analog chip that was designed as long as feed as long as ward artificial neural network operation. Ni1000 : (1996) Significantly more powerful than ETANN, however has narrower functionality SYNAPSE-1 System Architecture Image from [6] SYNAPSE-1 is a modular system arranged as a 2D array of MA16s, weight memories, data units, in addition to a control unit Modern Architectures: Custom Circuits

Neurogrid (2005) Neurogrid is a multi-chip system developed by Kwabena Boahen in addition to his group at Stan as long as d University [9] Objective is to emulate neurons Composed of a 4×4 array of Neurocores Each Neurocore contains a 256×256 array of neuron circuits with up to 6,000 synapse connections The FACETS Project (2005) Fast Analog Computing with Emergent Transient States (FACETS) A project designed by an international collective of scientists in addition to engineers funded by the European Union Recently developed a chip containing 200,000 neuron circuits connected by 50 million synapses. Image from [9] Torres-Huitzil: FPGA Model Torres-Huitzil et. al (2005) designed an hardware architecture as long as a bio-inspired neural model as long as motion estimation. Architecture has 3 basic components which per as long as m spatial, temporal, in addition to excitatory-inhibitory connectionist processing. Observed approximately 100 x speedup over Pentium 4 processor implementation as long as 128×128 images

CMOL based design Developed by Dan Hammerstrom HTM on FPGAs Implemented on a Cray XD1 PEs on FPGA

Large Scale Simulations IBM: Blue Brain Project: IBM & EPFL (Switzerl in addition to ) IBM Almaden Research Center Los Alamos National Lab Air Force Research Laboratory (Rome, NY) Academia: Portl in addition to State University Royal Institute of Technology (KTM, Sweden) AFRL PS3 Cluster For more in as long as mation Visit Institute of Neuromorphic Engineering: http://www.ine-web.org/

References [1] Neuromorphic, . [2] Hammerstrom, D. “A Survey of Bio-Inspired in addition to Other Alternative Architectures,” in Waser, Rainer (ed.) Nanotechnology. Volume 4: In as long as mation technology II. Weinheim: Wiley-VCH, pp. 251-282, 2006. [3] Carver Mead, [4] Holler, M., et al. “An Electrically Trainable Artificial Neural Network (ETANN) with 10240 “Floating Gate” Synapses,” International Joint Conference on Neural Networks, 1989. [5] Nestor, I., Ni1000 Recognition Accelerator – Data Sheet, 1-7, 1996. [6] Ramacher, U. et al. “SYNAPSE-1: a high-speed general purpose parallel neurocomputer system, “ IPPS ( 774-781). 1995. References [7] R. Serrano-Gotarredona, T. et al. “A Neuromorphic Cortical Layer Microchip as long as Spike Based Event Processing Vision Systems,” IEEE Trans. on Circuits in addition to Systems, Part-I. Vol. 53, No. 12, pp. 2548-2566, December 2006. [8] Serrano-Gotarredona, R., et al. “AER Building Blocks as long as Multi-Layer Multi-Chip Neuromorphic Vision Systems,” , Advances in Neural In as long as mation Processing Systems (NIPS), 18: 1217-1224, Dec, Y. Weiss in addition to B. Schölkopf in addition to J. Platt (Eds.), MIT Press, 2005 [9] Brains in Silicon,. [10] FACETS: Fast Analog Computing with Emergent Transient States, . [11] Graham-Rowe, D. “Building a Brain on a Silicon Chip,” in Technology Review, March 25, 2009. [Online]. Available: . [ Accessed March 28, 2009]. [12] C. Torres-Huitzil, et. al. “On-chip Visual Perception of Motion: A Bio-inspired Connectionist Model on FPGA, “ Neural Networks Journal, 18(5-6):557-565, 2005.

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