Path: utzoo!utgpu!jarvis.csri.toronto.edu!mailrus!wasatch!sunset.utah.edu!u-jmolse
From: u-jmolse%sunset.utah.edu@wasatch.utah.edu (John M. Olsen)
Newsgroups: comp.sys.amiga
Subject: Re: blitting a neural net.
Keywords: Neural nets, blitter, life, pickled cumquats
Message-ID: <1870@wasatch.utah.edu>
Date: 20 May 89 20:32:29 GMT
References: <1082@altos86.UUCP> <10650@orstcs.CS.ORST.EDU> <3029@cps3xx.UUCP> <610@censor.UUCP> <730@wsu-cs.uucp> <11269@netnews.upenn.edu>
Sender: news@wasatch.utah.edu
Reply-To: u-jmolse%sunset.utah.edu.UUCP@wasatch.utah.edu (John M. Olsen)
Organization: University of Utah, Computer Science Dept.
Lines: 59

In article <3029@cps3xx.UUCP>, golden@cps3xx.UUCP (golden james) writes:
> What about using the blitter to implement a neural network, since they
> usually suffer from hundreds of simple integer calculations?  Could you
> simply "blit" the network recursively to obtain a result?

In article <11269@netnews.upenn.edu> ranjit@grad2.cis.upenn.edu.UUCP
(Ranjit Bhatnagar) writes:
><McCullough-Pitts neurons>
>Since the implementation of such a restricted network consists of
>nothing but zillions of 1-bit adds and multiplies, and a relatively
>small number of integer operations, and any network
>that does anything reasonable is going to be very large,
      ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
>perhaps the
>blitter would actually be able simulate such a network very efficiently
>compared to a general purpose processor (like the 68000).  

Uh, have you seen the size of the networks that are used to do loan
application analysis?  A whopping 15-20 nodes.  Now if you want to talk
about things like image analysis and enhancement, or robot vision, you will
get lots of nodes.

> 
> Mike Golden
> Physiology Undergraduate
> Michigan State University

In some lost article, hugh@censor (Hugh D. Gamble) writes:
>That's something I thought of just after I discovered Tom's nifty keen
>life program that mapped so beautifully on to the blitter instructions.
>So I asked a few experts and they either said no without thinking,
>or thought for a second before saying the same thing.

Back when I thought (during a moment of madness) I might have some spare 
time, I thought about this, and came up with the idea of implementing a 
3-dimensional neural back propogation network with the blitter, and after 
doing some back-of-the-envelope calculations, figured it would be about 1/4 
to 1/2 as fast to teach when compared with professional net software on fast 
PCAT's, but after it's learned it's stuff, it would blaze away at *over* 
5-10X the typical speeds (number of nodes calculated per second).

I used the blitter to do lots of parallel addition in a BADGE killer demo 
entry last year (Where *ARE* those prizes, you guys?!), which helped to
inspire me.  The way I figure it, a large network using binary values is
equivelant to a not-so-large network using integer or floating point
weights and computation.

An additional blessing of using the blitter is storage space.  You can use
huge 2 or 3 dimensional bit arrays (called screens :^) and most of the code
is just interface stuff for the blitter.  That way, you can even watch
the network run. :^)

As a side note, the LIFE game is actally a neural net where each node
has the same rules and weights, and just takes input from it's neighbors.

+  /|  |    /||| /\|       | John M. Olsen, 1547 Jamestown Drive  +
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