neural netwrkng: optimze training bfore fltering complxity

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back2future
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neural netwrkng: optimze training bfore fltering complxity

Unread post by back2future » Sat May 05, 2018 8:53 pm

seems to be a growing market by 1:4 for next 3 years

machine learning for consumer markets ... neural networking ip's need trained dev frameworks and libs before being useful for performance and consumption improvements
https://en.wikipedia.org/wiki/Convoluti ... al_network

[ xu4, tensorflow, near realtime object_identification 2017: viewtopic.php?f=95&t=28177 ]

ASword
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Re: neural netwrkng: optimze training bfore fltering complxi

Unread post by ASword » Wed May 30, 2018 1:04 am

The upcoming generation of SoCs appear to have quite a few with dedicated "neural processing" engines. The RK3399-AI has an "NPU" (not to be confused with _network_ processing unit) rated at 2.4 TOPS and very low power draw -- quite amazing. Only suited to inference, however, so training will need to continue to be done elsewhere (i.e. a more powerful machine, or else on the GPU/CPU of the SoC) and in the background while inferencing runs in realtime.

back2future
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Re: neural netwrkng: optimze training bfore fltering complxi

Unread post by back2future » Thu Sep 27, 2018 6:42 am

AI gets social dimensions <- therefore we have to define our complex societies for numerical limitation/distortion
Teaching Machines ‘Fairness’
Junko Yoshida
https://www.eetimes.com/document.asp?doc_id=1333748

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