Yeah it would have to be almost real-time to be used in instant replays.
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I’m pretty sure he was using the slide to show that RT has been accelerated (the time graphs), not what the real layout looks like.
If your technical judgement is so clouded by emotions, why bother to engage in these kind of discussion?
Anything based on Deep Learning has also the potential of generating artefacts.
This for rare cases not seen during the training.
If you look at the DL slomo footage of the falling ice hockey skater, there are huge tearing artefacts on the skates.
As good as it looks, it might not be good enough to cover everything.
That deep slomo network requires at least an order of magnitude more calculations and cannot be done in real time. (At least not on a single GPU.)
It’s a large, deep network.
Here’s the paper: https://arxiv.org/pdf/1712.00080.pdf
Cue Jensen "The more you buy the more you save"
I assume comes down to if the spanking new large DGX node can be centralised with the broadcasting station and away from the hires TV/film camera feed giving greater flexibility.
Those hires broadcast cameras used for best fidelity in sports I think (used to be) are shockingly expensive, even without considering aspects of high speed motion capturing with slo-mo playback.
I am very confused about what part of the denoising is done with the help of AI instead of normal shaders doing the work.
In the quadro turing presentation they showed that only global illumination is being denoised by a AI based denoiser and reflections and all other light effect with compute. But the slide at the turing event had no single mention of AI denoising , not even for global illumination.
Are the tensor cores too slow for it in realtime gaming situations ?
In the quadro turing presentation they showed that only global illumination is being denoised by a AI based denoiser and reflections and all other light effect with compute. But the slide at the turing event had no single mention of AI denoising , not even for global illumination.
Are the tensor cores too slow for it in realtime gaming situations ?
Yes they are used for that but Jensen also suggested it was used for raytrace denoising or attempt could be in some capability. Nvidia is a bit vague about it.
Maybe it just something they are still developing and we will see AI based denoising of raytracing at a later time.
You seem to imply Tensors couldn't be used for both?
No I am saying they could be but during the presentation the slide said they don't, which would conclude that they only use the tensor cores for DLSS.
Ike Turner
Veteran
The Tensor Cores are used for Denoising is practically all of the RT demos shown Pica Pica, Star Wars, Cornell box, etc etc) Now can they do both Denoising and DLSS at the same time? We don't know yet.
https://image.slidesharecdn.com/jhh...rce-rtx-launch-event-21-638.jpg?cb=1534805756
This slide seems to imply they don't.
Compare that to the quadro presentation slide where it does say AI below global illumination.
Honestly, that's what I expect from the 7nm TU102 replacement:
- Increased clockspeed to around 2350-2500 MHz boost, 2050 base
- 2 RT cores per SM
- 64 SMs (4096 cc)
- 16GB HMB2 or 24GB GDDR6
- Identical tensor core count
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Wonder if it's a future feature since it's included in the NGX SDK. Currently there is only details for DLSS and AI Painting, but also included in the stack are placeholders for AI Slow-Mo and AI Res-Up.
https://developer.nvidia.com/rtx/ngx
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A theoretical 18Gb/384-bit part would see memory bandwidth up to 864GB/s from 616GB/s, a healthy 40% increase and well within the realm of possibility.
That's true, but I'm just not expecting that in the first round of 7nm chips.
Way too much chit-chat, IMO, and little detail about the questions asked, actually.
And it's kind of annoying this recent attitude of "thanks to RT everything feels real and like you're actually in the game, so before this moment everything was crap".
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