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0 pointsfchollet10y ago0 comments

These two things are non-trivial, but not particularly hard in themselves. However, doing them at ultra-low latency becomes quite a challenge. Doing anything at ultra-low latency is already a challenge, but especially so when what you're trying to do is running a deep neural net for entity recognition or gesture recognition.

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woodman10y ago

Training an ANN is computationally intensive, using a trained ANN is not. No context switching for system calls, no memory management, just matrix math.

ansgri10y ago

well, first you need to know what image regions feed to ANN, and that can involve some segmentation and pre-recognition, otherwise you're going to evaluate the net at all feasible subwindows — and that's a LOT of matrix math for you. Very big GPU can help, but they have latency in themselves, and FPGA at such performance levels are inordinately expensive. Done at scale though ASICs seem to be the sure-to-work way.

woodman10y ago

I'd be very surprised if a modern cpu couldn't handle the task, especially if you were clever about detecting regions of interest, predicting head movement and cache maintenance. But I'd also be surprised if they go to market with an x86 under the hood.

I remember reading a while ago about how smart tvs were using ANNs for upscaling, so it has been done at scale. rimshot

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0 pointsfchollet10y ago0 comments

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woodman10y ago

Training an ANN is computationally intensive, using a trained ANN is not. No context switching for system calls, no memory management, just matrix math.

ansgri10y ago

woodman10y ago

I remember reading a while ago about how smart tvs were using ANNs for upscaling, so it has been done at scale. rimshot

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