Download Analog VLSI Neural Networks: A Special Issue of Analog by Yoshiyasu Takefuji (auth.), Yoshiyasu Takefuji (eds.) PDF

By Yoshiyasu Takefuji (auth.), Yoshiyasu Takefuji (eds.)

This booklet brings jointly in a single position very important contributions and state of the art learn within the speedily advancing zone of analog VLSI neural networks.
The booklet serves as a good reference, offering insights into probably the most vital concerns in analog VLSI neural networks examine efforts.

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Extra resources for Analog VLSI Neural Networks: A Special Issue of Analog Integrated Circuits and Signal Processing

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The sizes of the transistors M6 and M8 are fully determined by the desired resistance value. As a first approximation-the error introduced by this approximation is evaluated in Section 8-it can be assumed that the current dividers are implemented as simple current mirrors so that the equations for the transistor area remain valid. For the given bias current and for the desired time constants M6 and M8 are long transistors. D. Kinget was born in Buffalo, NY, in 1967. Sc. degree in electrical and mechanical engineering in 1990 from the Katholieke Universiteit Leuven, Belgium.

Methods for arrangement of these weights into a sparse pseudorandom interconnection matrix, and for parallel implementation of the learning rule, are described. Test results from functional blocks on first silicon are presented. It is estimated that a network with upwards of 50K weights and with submicrosecond settling times could be built with a conventional CMOS double-poly process and die size. 1. Introduction In recent years, interest in neural networks and neuralnetwork-like computational models has seen a major resurgence, due at least in part to the prospect of compact and dense implementation of these networks in analog integrated circuit form.

The big difference for the Rarea value can be explained from the simplification made for the current mirror layout. The use of a centroid layout and of a Full Analog CMOS Integration of Very Large Time Constants for Synaptic Transfer portional to the square root of the time constant and thus inversely proportional to the square root of the cutoff frequency. Consequently, the factor Area· expresses the area efficiency of the implementation independent of the cutoff frequency. The offset of a time constant circuit grows linearly with the amount of electronic multiplication used as can be seen from equations (4) and (5).

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