From: Dani Eder (firstname.lastname@example.org)
Date: Thu Jan 12 2006 - 08:14:47 MST
> On my machine, a 3GHz workstation, im able to run a
> feedforward network at
> about 150.000 operations /second WITH
> training(backprop) .. take training
> out of the equation and we may, lets shoot high,
> land on 1 million 'touched'
> neurons/second .. now from 10^6 -> 10^14 .. that's
> one hell of a big
You are losing a lot of orders of magnitude by
a neural network on a Von Neuman architecture. Taking
reverse, our brains, which are neural nets, can
a VN machine at about 1 op/sec (doing mathematical
We are much faster at things our brains are natively
like vision and speech recognition.
It would be a fairer comparison to compare the
capability of a workstation (~3 x 10^9/sec) to the
synapse firing rate. This implies that AI software
be designed to make efficient use of the CPU's
or alternatively, a 'neural net card' was installed to
accelerate that type of calculation. This would be
to graphics accelerators that are specialized for one
set of calculations.
A high end workstation, with four dual-core
have a theoretical rate of 24x10^9/sec, or a factor of
the synapse rate equivalent.
When I make these type of calculations, I use a factor
below and above the raw synapse rate to allow for
in how much data one synapse firing represents.
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