Early Benchmarking Results for Neuromorphic ComputingDESMOND YUEN
An update on the Intel Neuromorphic Research Community’s growth and benchmark results, including the addition of new corporate members and numerous new benchmarking updates computed on Intel’s neuromorphic test chip, Loihi.
Early Benchmarking Results for Neuromorphic ComputingDESMOND YUEN
An update on the Intel Neuromorphic Research Community’s growth and benchmark results, including the addition of new corporate members and numerous new benchmarking updates computed on Intel’s neuromorphic test chip, Loihi.
ARTIFICIAL brain ......AI
Artificial brain (or artificial mind) is a term commonly used in the media[1] to describe research that aims to develop software and hardware with cognitive abilities similar to those of the animal or human brain. Research investigating "artificial brains" and brain emulation plays three important roles in science:
An ongoing attempt by neuroscientists to understand how the human brain works, known as cognitive neuroscience.
A thought experiment in the philosophy of artificial intelligence, demonstrating that it is possible, at least in theory, to create a machine that has all the capabilities of a human being.
A long term project to create machines exhibiting behavior comparable to those of animals with complex central nervous system such as mammals and most particularly humans. The ultimate goal of creating a machine exhibiting human-like behavior or intelligence is sometimes called strong AI.
An example of the first objective is the project reported by Aston University in Birmingham, England[2] where researchers are using biological cells to create "neurospheres" (small clusters of neurons) in order to develop new treatments for diseases including Alzheimer's, motor neurone and Parkinson's disease.
The second objective is a reply to arguments such as John Searle's Chinese room argument, Hubert Dreyfus' critique of AI or Roger Penrose's argument in The Emperor's New Mind. These critics argued that there are aspects of human consciousness or expertise that can not be simulated by machines. One reply to their arguments is that the biological processes inside the brain can be simulated to any degree of accuracy. This reply was made as early as 1950, by Alan Turing in his classic paper "Computing Machinery and Intelligence".[3]
The third objective is generally called artificial general intelligence by researchers.[4] However, Ray Kurzweil prefers the term "strong AI". In his book The Singularity is Near, he focuses on whole brain emulation using conventional computing machines as an approach to implementing artificial brains, and claims (on grounds of computer power continuing an exponential growth trend) that this could be done by 2025. Henry Markram, director of the Blue Brain project (which is attempting brain emulation), made a similar claim (2020) at the Oxford TED conference in 2009.
Deep Convolutional Network evaluation on the Intel Xeon PhiGaurav Raina
With a sharp decline in camera cost and size along with superior computing power available at increasingly low prices, computer vision applications are becoming ever present in our daily lives. Research shows that Convolutional Neural Networks (ConvNet) can outperform all other methods for
computer vision tasks (such as object detection) in terms of accuracy and versatility.
One of the problems with these Neural Networks, which mimic the brain, is that they can be very demanding on the processor, requiring millions of computational nodes to function. Hence, it is challenging for Neural Network
algorithms to achieve real-time performance on general purpose embedded platforms.
Parallelization and vectorization are very e ective ways to ease this problem and make it possible to implement such ConvNets on energy efficient embedded platforms. This thesis presents the evaluation of a novel ConvNet for road speed sign detection, on a breakthrough 57-core Intel Xeon Phi
processor with 512-bit vector support. This mapping demonstrates that the parallelism inherent in the ConvNet algorithm can be e ffectively exploited by the 512-bit vector ISA and by utilizing the many core paradigm.
Detailed evaluation shows that the best mappings require data-reuse strategies that exploit reuse at the cache and register level. These implementations are boosted by the use of low-level vector intrinsics (which are
C style functions that map directly onto many Intel assembly instructions).
Ultimately we demonstrate an approach which can be used to accelerate Neural Networks on highly-parallel many core processors, with execution speedups of more than 12x on single core performance alone.
Intel's Nehalem Microarchitecture by Glenn Hintonparallellabs
Intel's Nehalem family of CPUs span from large multi-socket 32 core/64 thread systems to ultra small form factor laptops. What were some of the key tradeoffs in architecting and developing the Nehalem family of CPUs? What pipeline should it use? Should it optimize for servers? For desktops? For Laptops? There are lots of tradeoffs here. This talk will discuss some of the tradeoffs and results.
Lecture from week 5 from a college level neuropharmacology course taught in the spring 2012 semester by Brian J. Piper, Ph.D. (psy391@gmail.com) at Willamette University.
Cybernetic theory of craniofacial growth /certified fixed orthodontic courses...Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078
Cybernetic theory of craniofacial /certified fixed orthodontic courses by Ind...Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and
offering a wide range of dental certified courses in different formats.for more details please visit
www.indiandentalacademy.com
Lecture 7 from a college level neuropharmacology course taught in the spring 2012 semester by Brian J. Piper, Ph.D. (psy391@gmail.com) at Willamette University.
ARTIFICIAL brain ......AI
Artificial brain (or artificial mind) is a term commonly used in the media[1] to describe research that aims to develop software and hardware with cognitive abilities similar to those of the animal or human brain. Research investigating "artificial brains" and brain emulation plays three important roles in science:
An ongoing attempt by neuroscientists to understand how the human brain works, known as cognitive neuroscience.
A thought experiment in the philosophy of artificial intelligence, demonstrating that it is possible, at least in theory, to create a machine that has all the capabilities of a human being.
A long term project to create machines exhibiting behavior comparable to those of animals with complex central nervous system such as mammals and most particularly humans. The ultimate goal of creating a machine exhibiting human-like behavior or intelligence is sometimes called strong AI.
An example of the first objective is the project reported by Aston University in Birmingham, England[2] where researchers are using biological cells to create "neurospheres" (small clusters of neurons) in order to develop new treatments for diseases including Alzheimer's, motor neurone and Parkinson's disease.
The second objective is a reply to arguments such as John Searle's Chinese room argument, Hubert Dreyfus' critique of AI or Roger Penrose's argument in The Emperor's New Mind. These critics argued that there are aspects of human consciousness or expertise that can not be simulated by machines. One reply to their arguments is that the biological processes inside the brain can be simulated to any degree of accuracy. This reply was made as early as 1950, by Alan Turing in his classic paper "Computing Machinery and Intelligence".[3]
The third objective is generally called artificial general intelligence by researchers.[4] However, Ray Kurzweil prefers the term "strong AI". In his book The Singularity is Near, he focuses on whole brain emulation using conventional computing machines as an approach to implementing artificial brains, and claims (on grounds of computer power continuing an exponential growth trend) that this could be done by 2025. Henry Markram, director of the Blue Brain project (which is attempting brain emulation), made a similar claim (2020) at the Oxford TED conference in 2009.
Deep Convolutional Network evaluation on the Intel Xeon PhiGaurav Raina
With a sharp decline in camera cost and size along with superior computing power available at increasingly low prices, computer vision applications are becoming ever present in our daily lives. Research shows that Convolutional Neural Networks (ConvNet) can outperform all other methods for
computer vision tasks (such as object detection) in terms of accuracy and versatility.
One of the problems with these Neural Networks, which mimic the brain, is that they can be very demanding on the processor, requiring millions of computational nodes to function. Hence, it is challenging for Neural Network
algorithms to achieve real-time performance on general purpose embedded platforms.
Parallelization and vectorization are very e ective ways to ease this problem and make it possible to implement such ConvNets on energy efficient embedded platforms. This thesis presents the evaluation of a novel ConvNet for road speed sign detection, on a breakthrough 57-core Intel Xeon Phi
processor with 512-bit vector support. This mapping demonstrates that the parallelism inherent in the ConvNet algorithm can be e ffectively exploited by the 512-bit vector ISA and by utilizing the many core paradigm.
Detailed evaluation shows that the best mappings require data-reuse strategies that exploit reuse at the cache and register level. These implementations are boosted by the use of low-level vector intrinsics (which are
C style functions that map directly onto many Intel assembly instructions).
Ultimately we demonstrate an approach which can be used to accelerate Neural Networks on highly-parallel many core processors, with execution speedups of more than 12x on single core performance alone.
Intel's Nehalem Microarchitecture by Glenn Hintonparallellabs
Intel's Nehalem family of CPUs span from large multi-socket 32 core/64 thread systems to ultra small form factor laptops. What were some of the key tradeoffs in architecting and developing the Nehalem family of CPUs? What pipeline should it use? Should it optimize for servers? For desktops? For Laptops? There are lots of tradeoffs here. This talk will discuss some of the tradeoffs and results.
Lecture from week 5 from a college level neuropharmacology course taught in the spring 2012 semester by Brian J. Piper, Ph.D. (psy391@gmail.com) at Willamette University.
Cybernetic theory of craniofacial growth /certified fixed orthodontic courses...Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078
Cybernetic theory of craniofacial /certified fixed orthodontic courses by Ind...Indian dental academy
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Indian dental academy provides dental crown & Bridge,rotary endodontics,fixed orthodontics,
Dental implants courses.for details pls visit www.indiandentalacademy.com ,or call
0091-9248678078
The Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and
offering a wide range of dental certified courses in different formats.for more details please visit
www.indiandentalacademy.com
Lecture 7 from a college level neuropharmacology course taught in the spring 2012 semester by Brian J. Piper, Ph.D. (psy391@gmail.com) at Willamette University.
Cybernetics /certified fixed orthodontic courses by Indian dental academy Indian dental academy
Indian Dental Academy is the Leader in continuing dental education , training dentists in all aspects of dentistry and offering a wide range of dental certified courses in different formats.
Introduce F9 microkernel, new open source implementation built from scratch, which deploys modern kernel techniques, derived from L4 microkernel designs, to deep embedded devices.
:: https://github.com/f9micro
Characteristics of F9 microkernel
– Efficiency: performance + power consumption
– Security: memory protection + isolated execution
– Flexible development environment
An ANN depends on an assortment of associated units or hubs called fake neurons, which freely model the neurons in an organic cerebrum. Every association, similar to the neurotransmitters in an organic cerebrum, can send a sign to different neurons. A counterfeit neuron that gets a sign at that point measures it and can flag neurons associated with it.
Implementation of Feed Forward Neural Network for Classification by Education...ijsrd.com
in the last few years, the electronic devices production field has witness a great revolution by having the new birth of the extraordinary FPGA (Field Programmable Gate Array) family platforms. These platforms are the optimum and best choice for the modern digital systems now a day. The parallel structure of a neural network makes it potentially fast for the computation of certain tasks. The same feature makes a neural network well suited for implementation in VLSI technology. In this paper a hardware design of an artificial neural network on Field Programmable Gate Arrays (FPGA) is presented. Digital system architecture is designed to realize a feed forward multilayer neural network. The designed architecture is described using Very High Speed Integrated Circuits Hardware Description Language (VHDL).General Terms-Network.
Many emerging applications require methods tailored towards high-speed data acquisition and filtering of streaming data followed by offline event reconstruction and analysis. In this case, the main objective is to relieve the immense pressure on the storage and communication resources within the experimental infrastructure. In other applications, ultra low latency real time analysis is required for autonomous experimental systems and anomaly detection in acquired scientific data in the absence of any prior data model for unknown events. At these data rates, traditional computing approaches cannot carry out even cursory analyses in a time frame necessary to guide experimentation. In this talk, Prof. Ogrenci will present some examples of AI hardware architectures. She will discuss the concept of co-design, which makes the unique needs of an application domain transparent to the hardware design process and present examples from three applications: (1) An in-pixel AI chip built using the HLS methodology; (2) A radiation hardened ASIC chip for quantum systems; (3) An FPGA-based edge computing controller for real-time control of a High Energy Physics experiment.
1. SEASONs : Spiking, Entropic, Asynchronous, Self-Organizing Neural Networks On self-modifying ‘ machine learning ’ systems Ph.D. project: Ludovic A. Krundel Supervisors : Dr. David J. Mulvaney Dr. Vassilios A. Chouliaras Neural Networks with Cellular Automata
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4. CPU: insight introduction Sequencer (Automaton) Sequencer (Automaton) Processing Unit Memory Arithmetic and Logic Unit Instruction Register RI Exchange Unit Accus Exchange Register RE Address Register RAM Ordinal Counter CO Data or Instruction
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