Temporally Aware ML Model for Asynchronous Frame Generation
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Solution Overview
Problem
Current graphics processing systems face challenges in generating frames that are temporally aware and synchronized with a target frame pace, particularly in scenarios requiring asynchronous rendering and compute operations, where traditional methods struggle to maintain consistent frame pacing and efficient processing.
Innovation Solution
A temporally aware machine learning model is trained to generate frames based on input frames and optical flow data, with the capability to evolve optical flow to target a specific future timestamp, enabling time-based frame generation that is asynchronous to the render rate.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional rendering methods are used to generate frames, then the processing can be performed at a fixed render rate, but the frame generation cannot maintain consistent frame pacing when asynchronous operations are required
Solution Approach 1:
The system performs preliminary actions by generating multiple future frames in advance using a temporal neural network model, allowing the renderer to maintain consistent frame pacing without waiting for asynchronous compute operations to complete. The model predicts future frame content based on historical frames and optical flow, enabling proactive frame generation that maintains timing consistency.
Solution Approach 2:
A temporal neural network model acts as an intermediary between the renderer and the display output. This intermediary component generates frames asynchronously from input frames and optical flow data, then feeds them to the renderer at the required display rate, decoupling the render rate from the display rate and enabling consistent frame pacing regardless of asynchronous processing speed.
2Productivity
If frames are generated asynchronously from the render rate, then processing efficiency can improve, but temporal awareness and synchronization with target frame pace become difficult to maintain
Solution Approach 1:
The system incorporates feedback mechanisms where the temporal neural network receives actual rendered frames and display timing information as input, then adjusts its frame generation accordingly. The model uses optical flow data that captures temporal relationships between frames, allowing it to synchronize generated frames with the target frame pace while maintaining asynchronous processing.
Solution Approach 2:
The frame generation system dynamically adapts to changing render rates and display requirements. The temporal neural network adjusts its predictions and frame generation timing based on real-time feedback from the rendering pipeline, enabling flexible asynchronous operation that maintains temporal synchronization with the target frame pace.
3Adaptability or versatility
If a temporal neural network model is introduced to generate frames based on optical flow, then time-based frame generation capability is achieved, but the device complexity increases
Solution Approach 1:
The temporal neural network model serves multiple functions within the graphics processing system: it generates future frames from historical frames, computes optical flow between frames, and synchronizes frame timing. By consolidating these functions into a single multi-functional component, the system achieves time-based frame generation capability while minimizing the increase in overall device complexity.
Data Source
AI summary
Described herein is a graphics processor configured to perform time based frame generation via a temporally aware machine learning model that enables the generation of a frame at a target timestamp relative to the render times of input frames. For example, for an extrapolated frame generated by the temporally aware machine learning model, a low relative timestamp would indicate that the extrapolated frame will appear close in time after the final frame in a sequence of frames and should be relatively close in appearance to the final frame. A higher relative timestamp would indicate that the extrapolated frame should depict a greater degree of evolution based on the optical flow.