Distributed Denoising via Ghost Region Data Exchange
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Solution Overview
Problem
Existing denoising frameworks for real-time ray tracing operate on a single machine, limiting their ability to access all rendered pixels for computing a denoised image when rendering is done across multiple devices, and they require extensive training data that may not generalize well to new scenarios.
Innovation Solution
A distributed denoising algorithm that uses machine learning, where nodes exchange 'ghost region' data to perform denoising operations, and a machine learning engine is continually trained and updated during runtime using a sub-region of the image, allowing it to adapt to new data and improve denoising quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If rendering is distributed across multiple devices, then rendering capacity and speed are improved, but access to all rendered pixels for denoising becomes limited
Solution Approach 1:
The patent divides the rendering system into multiple distributed devices (render nodes), each handling a subset of pixels. Each node processes its local pixels independently while exchanging only necessary boundary information with neighboring nodes, enabling parallel processing without requiring centralized access to all pixels.
Solution Approach 2:
The denoising operation is performed locally at each render node using only the pixels available at that node, rather than requiring global access to all rendered pixels. This local processing approach maintains denoising quality while respecting the distributed architecture constraints.
2Loss of information
If existing denoising frameworks operate on a single machine, then access to all rendered pixels is ensured, but scalability to distributed systems is limited
Solution Approach 1:
The patent designs a denoising framework that functions universally across both single-machine and multi-machine configurations. The same core algorithm operates in both environments, with automatic adaptation to the available pixel data scope, making the system versatile across different deployment scenarios.
Solution Approach 2:
The patent transitions the denoising system from a single-dimension (single machine) architecture to a multi-dimensional (distributed network) architecture. By adding the network dimension, the system gains scalability while maintaining core functionality through localized operations and selective data exchange.
3Manufacturing precision
If extensive training data is used for denoising, then denoising quality is improved, but generalization to new scenarios deteriorates
Solution Approach 1:
The patent implements a dynamic denoising approach where the model adapts its parameters during runtime based on the actual rendered data characteristics. This allows the system to maintain high denoising quality for the current scene while remaining adaptable to new scenarios, avoiding the overfitting problem of static extensively-trained models.
Solution Approach 2:
The denoising model performs self-adjustment during runtime by learning from the actual pixel data it processes, rather than relying solely on pre-trained extensive datasets. This self-service learning mechanism enables the model to generalize better to new scenarios while maintaining quality on current data.
Data Source
AI summary
An apparatus and method for hardware page cache migration. For example, one embodiment of an apparatus comprises: a memory management unit (MMU) to manage memory page migration in multi-processor environments in which multiple processors share a virtual memory address space, the memory page migration comprising movement of one or more memory pages from a local memory of a first processor to a local memory of a second processor; a central page cache integral to or coupled to the MMU, the central page cache to store memory pages based on requests generated from one or more of the multiple processors; access pattern detection circuitry/logic to detect data access patterns associated with data access requests from one or more of the multiple processors; and an adaptive page prefetcher to prefetch one or more memory pages to the central page cache responsive to the access pattern detection circuitry/logic detecting one of the data access patterns.