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VLSI Design of Neural Networks (Paperback, Softcover reprint of the original 1st ed. 1991)
Loot Price: R1,493
Discovery Miles 14 930
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VLSI Design of Neural Networks (Paperback, Softcover reprint of the original 1st ed. 1991)
Series: The Springer International Series in Engineering and Computer Science, 122
Expected to ship within 10 - 15 working days
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The early era of neural network hardware design (starting at 1985)
was mainly technology driven. Designers used almost exclusively
analog signal processing concepts for the recall mode. Learning was
deemed not to cause a problem because the number of implementable
synapses was still so low that the determination of weights and
thresholds could be left to conventional computers. Instead,
designers tried to directly map neural parallelity into hardware.
The architectural concepts were accordingly simple and produced the
so called interconnection problem which, in turn, made many
engineers believe it could be solved by optical implementation in
adequate fashion only. Furthermore, the inherent fault-tolerance
and limited computation accuracy of neural networks were claimed to
justify that little effort is to be spend on careful design, but
most effort be put on technology issues. As a result, it was almost
impossible to predict whether an electronic neural network would
function in the way it was simulated to do. This limited the use of
the first neuro-chips for further experimentation, not to mention
that real-world applications called for much more synapses than
could be implemented on a single chip at that time. Meanwhile
matters have matured. It is recognized that isolated definition of
the effort of analog multiplication, for instance, would be just as
inappropriate on the part ofthe chip designer as determination of
the weights by simulation, without allowing for the computing
accuracy that can be achieved, on the part of the user."
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