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Subject: Re: chess and neural networks

Author: Christophe Theron

Date: 21:25:24 07/04/03

Go up one level in this thread

On July 04, 2003 at 23:56:34, Vincent Diepeveen wrote:

>On July 04, 2003 at 11:32:03, Christophe Theron wrote:
>>On July 03, 2003 at 15:44:44, Landon Rabern wrote:
>>>On July 03, 2003 at 03:22:15, Christophe Theron wrote:
>>>>On July 02, 2003 at 13:13:43, Landon Rabern wrote:
>>>>>On July 02, 2003 at 02:18:48, Dann Corbit wrote:
>>>>>>On July 02, 2003 at 02:03:20, Landon Rabern wrote:
>>>>>>>I made an attempt to use a NN for determining extensions and reductions.  It was
>>>>>>>evolved using a GA, kinda worked, but I ran out of time. to work on it at the
>>>>>>>end of school and don't have my computer anymore. The problem is that the NN is
>>>>>>>SLOW, even using x/(1+|x|) for activation instead of tanh(x).
>>>>>>Precompute a hyperbolic tangent table and store it in an array.  Speeds it up a
>>>>>Well, x/(1+|x|) is as fast or faster than a large table lookup.  The slowdown
>>>>>was from all the looping necessary for the feedforward.
>>>>A stupid question maybe, but I'm very interested by this stuff:
>>>>Do you really need a lot of accuracy for the "activation function"? Would it be
>>>>possible to consider a 256 values output for example?
>>>>Would the lack of accuracy hurt?
>>>>I'm not sure, but it seems to me that biological neurons do not need a lot of
>>>>accuracy in their output, and even worse: they are noisy. So I wonder if low
>>>>accuracy would be enough.
>>>There are neural net models that work with only binary output.  If the total
>>>input value exceeds some threshhold then you get a 1 otherwise a 0.  The problem
>>>is with training them by back prop.  But in this case I was using a Genetic Alg,
>>>so no back prop at all - so no problem.  I might work, but I don't see the
>>>benefit - were you thinking for speed?  The x/(1+|x|) is pretty fast to
>>>calculate, but perhaps the binary (or other discrete) would be faster.
>>>Something to try.
>>Yes, what I had in mind was optimization by using integer arithmetic only.
>>If the output is always on 8 bits, the sigma(W*I) (weight*input) can be computed
>>on 32 bits (each W*I will have at most 16 bits).
>>Actually sigma(W*I) will have no more than 20 bits if each neuron has at most 16
>>inputs. 32 bits allows for 65536 input per neuron.
>>This -maybe- allows for a fast table lookup of the atan function that I see used
>>often in ANN. I think it can be a little faster than x/(1+|x|) computed using
>>floating point arithmetic. Also, and this is even more important, the sigma(W*I)
>>would use integer arithmetic instead of floating point.
>>Maybe I should just do a Google search for this, I'm sure I'm not the first one
>>to think about this optimzation.
>I'm actually sure you are the first to find this optimization!
>The reason is that the average AI scientist never is doing many practical
>experiments with ANNs. Basically practical researchers outside ANNs are doing
>somteimes a few experiments like you and i. Further from that very tiny
>percentile researchers that sometimes do ANN experiments the average solution
>they come up with when they need to calculate it faster is either ask some
>system time of a supercomputer, or more likely fill a sporthal of their own
>university department with PC's, then they lay down a few network cables under
>the floor and they run a few carefully selected benchmarks showing their beowulf
>cluster really is a great thing to have.
>When a year or 10 ago some dude was looking around for funding for either a
>supercomputer or to buy hardware to speedup his neural networking software which
>was using quite some neurons, then i have translated his quickbasic program into
>C and optimized its speed by writing out stuff and finding clever loops within
>it that lossless speeded it up.
>In total i managed to speedup his software around a factor 1000 after 7 days of
>hard work (remember i started with 100KB quickbasic code and ended up with about
>20KB C code. note that the quickbasic used was the compilerversion not an
>I was very amazed then that he didn't like me doing that, because i had thought
>he just wanted his software to run faster. When you grow up you slowly learn how
>people work and in the AI world this is pretty easy to predict.
>So having that in mind i am sure that you are one of the first to publicly speak
>out and say that you can speedup things a lot!
>Last tuesday i was at a supercomputing conference and of course for hours i have
>talked with many researchers and professors. I am still very proud that against
>no one after talking what they did on the computer i told to that i would love
>to take a look at their code in order to give them a few free tips to speedup
>their software quite some times. With some of them i sure knew i could.
>Some still haven't found out the difference between latency and bandwidth and
>what is out of order (the R14k processors) and that the new itanium2 processors
>here (416 of them clocked 1.3Ghz and 832GB ram) which are way faster for
>floating point and way slower for latency than the old R14ks.
>Possible the slow latency is partly because of the interesting idea to run
>redhat 7.2 with the unmodified linux kernel 2.4.19 at it. Let's blame the
>economic times that causes this Dutch habit to save money :)
>A good example of several different research projects there which i can speedup
>with just 5 minutes of work is that several projects lose like all of their
>system time to a RNG as for each number in the calculation matrix they take a
>number from the RNG.
>They compile of course with option -O2.
>Their RNG is some slow thing that runs in 32 bits.
>However for my latency test i did a very small effort to speedup an already fast
>RNG a little. Replacing their RNG by this one would speedup their field
>calculations quite a lot.
>The matrix calculations they do then are pretty fast by the way as an efficient
>library is doing them for them.
>However they could also speed the entire program up incredibly by using 64 bits
>integer values instead of floating point.
>Remember both are 64 bit processors. Both the R14k (8MB L2 cache) and the
>I2-Madisons which are 3MB L2 cache.
>The research these guys do then still is very good research.
>No i won't mention a single name of the guys. They are cool guys.
>Best regards,

But I have seen some commercial ANN applications out there. Surely these have
optimized integer arithmetic, because there must be an economical incentive to
do so.


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