AMD FSR Redstone uses machine learning to achieve parity with Nvidia DLSS

IbizaPocholo

NeoGAFs Kent Brockman

As part of its Computex 2025 announcements, AMD has given gamers a sneak peek at the company's major update for its FidelityFX Super Resolution (FSR) technology. Dubbed FSR Redstone, the upcoming installment will bring many new features to match rival Nvidia's Deep Learning Super Sampling (DLSS).

AMD is already plotting ahead and preparing FSR Redstone as the next substantial upgrade for FSR.

Although AMD did not provide specific details, the chipmaker emphasized three features to be included in FSR Redstone: neural radiance caching, machine learning ray regeneration, and machine learning frame generation. Some of these features might sound familiar, as they are already part of the Nvidia DLSS suite.

AMD states that neural radiance caching effectively learns how light reflects within a scene. The objective is to predict and store indirect lighting assets in a cache, which can subsequently be used to generate heaps of other rays. Logically, this helps accelerate path tracing.

Ray regeneration is equivalent to Nvidia's ray reconstruction. This feature leverages a trained neural network to regenerate pixels that couldn't be accurately traced. Thanks to machine learning, it can predict and filter grainy noise in real time.
 
It's already pretty hard to tell between their quality settings. It's time to compete on how far those internal resolutions can be pushed down.
 
Competition is good. Hopefully they can achieve parity with DLSS4. By the time of my next GPU purchase they may become a contender.
 
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Parity? I'll believe it when I see it. AMD's got a lot of work to do, but I hope they and Intel progress far enough in the GPU space to bring some proper competition into the arena.
 
So are these algorithms doing the similar to this? Or are they just the mathematical magic described in the video dressed up as AI?

 
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