In McCulloch and Pitts elaborated model of human and animal neuron and explained the principles of combination of neurons i. Further advancement in this field of science contributed to introduction and design of perceptron Rosenblatt, Its task was to recognise alphanumeric signs. There were also attempts to use neural networks among other things to weather forecast, identification of mathematical formulas, or analysis of electrocardiogram.
DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement | Research
In Minski and Papert published the monograph in which they proved that one-layer perceptron-like nets has limited area of application. This fact discouraged scientists from working on perceptrons and moved theirs interests towards expert systems. In the middle of the eighties some papers which proved that multi-layer non-linear neural networks has not limitations appeared. It caused growth of interests of this field of knowledge. The technology development of VLSI integrated circuits contributed to improvement of neuro-computers in the same period of time.
VLSI for Neural Networks and Artificial Intelligence
The very important achievements are different training methods of multi-layer neural networks, e. There is about 10 15 connections synapses between the cells. The neuron works with the frequency from 1 to Hz. Consequently the approximated rate equals about 10 18 operations per second and is many times greater than features of nowadays computers. Neural network is a simple model of brain.
see It consists of great number of neurons i. Neurons are formed in specified manners see 'Neural Networks'. Such element consists of several inputs.
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The goal of this course is to bring together world-renowned researchers and the brightest postgraduate research students and professionals in a weeklong intensive course where participants exchange ideas and experiences in developing and deploying state-of-the-art acceleration technologies focusing mainly on neural network systems and their applications.
During the week, experts from around the world will be delivering lectures and hands-on exercises on cutting-edge acceleration technologies targeting FPGA platforms.
Through these exercises, participants get to experience first-hand the potential of using application accelerators in their research. Participants also get a chance to showcase their latest research, and discuss their need for high-performance computers through poster sessions. Enrollment will be on a competitive basis. Philip Leong University of Sydney, Australia. Jason Anderson University of Toronto, Canada.