引用:

In certain example embodiments, the “game device” may be a device that is hosted within a cloud-based environment (e.g., on Amazon's AWS or Microsoft's Azure system). In such a scenario, the game (or other application program) may be hosted on a virtual machine in the cloud computer system and the input devices and display devices may be local the user. The user may also have a “thin” client application or computer that is communicating with the cloud-based service (e.g., communicate data from the device and receive and display images that are received from the cloud to the television). In this type of implementation, user input is passed form the user's computer/input device to the cloud-based computer system that is executing the video game application 108. Images are generated by the game engine, transformed by the neural network (e.g., upconverted) and then transmitted to the user's display (or a computer that then outputs the images to the display).

我惊了……
不仅仅用于本地渲染的游戏，还能用于云游戏。

引用:

In certain example embodiments, the techniques herein may advantageously take advantage of NVIDIA's tensor cores (or other similar hardware). A tensor core may be a hardware unit that multiplies two 16×16 FP16 matrices (or other sized matrices depending on the nature of the hardware), and then adds a third FP16 matrix to the result by using fused multiply—add operations, and obtains an FP16 result. In certain example embodiments, a tensor core (or other processing hardware) can be used to multiply two 16×16 INT8 matrices (or other sized matrices depending on the nature of the hardware), and then add a third INT32 matrix to the result by using fused multiply-add operations and obtain an INT32 result which can then be converted to INT8 by dividing by the appropriate normalization amount (e.g., which may be calculated during a training process, such as described in connection with FIG. 9). Such conversions may be accomplished using, for example, a low processing cost integer right shift. Such hardware acceleration for the processing discussed herein (e.g., in the context the separable block transforms) may be advantageous.

这一段已经明说了，基于NV张量核心。
作为任天堂专利倒也没说死，or other similar hardware。
但肯定是依赖硬件深度计算加速的，也就是AMD是没戏的。
这也是任天堂相对于steam deck最大竞争优势了。

分析这事，还得看泥潭卖哥。

posted by wap, platform: VIVO
540p。。难道掌机模式也能dlss了？