I tried to build your project but ran into an error. I filed the bug with
the make error. Would love to see it in action!

Ian


On Thu, May 8, 2014 at 2:53 PM, Ian Danforth <[email protected]>wrote:

> Joonas,
>
>  Really really cool and the name is perfect, well done :)
>
> Ian
>
>
> On Thu, May 8, 2014 at 2:51 PM, Joonas Haapala <[email protected]> wrote:
>
>> Hi,
>>
>> I did some work six months ago in porting the whitepaper to OpenCL. The
>> WIP code is available here: https://github.com/Jontte/CortiCL
>>
>> CLA doesn't port to GPU as well as deep neural networks due to some parts
>> of the algorithm needing memory access to the neighbouring columns
>> (neighbourhood inhibition in SP) and the need to maintain variable sized
>> memory buffers (list of proposed changes to a segment), but it all can be
>> worked around.
>>
>> CPU<->GPU memory bandwidth shouldn't be a problem, since the state of the
>> whole network doesn't have to be accessible from the CPU side during
>> operation unless one wants debug diagnostics. In my application I only move
>> the network input/output bit patterns across the boundary each step:
>> https://github.com/Jontte/CortiCL/blob/master/src/cltemporal.cpp#L83
>>
>> Joonas Haapala
>>
>>
>>
>>
>> 2014-05-05 8:28 GMT+03:00 Sergey Bryukov <[email protected]>:
>>
>>>  Hi, is there any progress for CLA on GPU?
>>>
>>> There is a mention of GPU data base. Dont know if it would be useful for
>>> CLA.
>>>
>>> "Known as MapD <http://geops.csail.mit.edu/docs/mapd_overview.pdf>, or
>>> massively parallel database, the new technology achieves big speed gains by
>>> storing the data in the onboard memory of graphics processing units (GPUs)
>>> instead of in central processing units (CPUs), as is conventional."
>>>
>>>
>>> http://www.technologyreview.com/news/520021/graphics-chips-help-process-big-data-sets-in-milliseconds/
>>>
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>>>
>>
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>
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