Magnus, that is very interesting. Am I getting this right - you are doing
your own version of CLA. Parts of it are GPU accelerated using bitmap
processing with shaders/textures? Can you tell us which parts are on the
GPU and how the data is structured? Any metrics on how much speedup you are
getting? Is latency from CPU to GPU a problem? It sounds like you allocate
all the memory for data structures regardless of whether they are used
(connected synapses) or not. This sounds like it might be a good tradeoff
and makes the data more GPU friendly. It also sounds like this approach of
using texture/shaders makes it scalable via SLI to add more GPU power by
installing more GPU cards - no code changes necessary.

We would all be very interested in seeing a sketch / outline of how you are
doing this. It sounds like the convolution approach that Greg is speaking
of that is used for deep nets. I was under the impression that the CLA does
not map well to convolution. I hope I am wrong and we can find a way
restructure our data and algorithm to do convolution / shader operations.
Perhaps allocating temporal pooler memory for all potential weights instead
of growing memory over time like Ian suggests.

Please share further details.


On Wed, May 7, 2014 at 11:04 PM, Magnus McWootton <[email protected]>wrote:

> Ive done something crazy, because I make video games.
> ive written a spacial system in direct x 11... i do with 16 synapses per
> cell, and i can go up to about an 8192x8192 texture, 16 synapses per cell,
> means its 2048x2048 cells.
> If you do it purely on textures only, then itll work with SLI.  which is
> pretty important.
> Ive got a hierarchy, its not totally working yet, but I cycle pretty
> quick... I dont think a cuda one would beat it,  writing it in shaders is
> even more restrictive.
> The synapses all have an address, and a weight,   when scoring your cells,
> you literally brute force every synapse.  the gpu does a good job at it.
>
>
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