Hardware-Accelerated KNN Queries via Animated Bounding Boxes
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies face challenges in efficiently performing K-nearest neighbor (KNN) queries, particularly in handling arbitrary data primitives and varying search radii, which limits their performance in applications like image processing and autonomous vehicles.
Innovation Solution
The use of hardware-accelerated light transport simulation techniques, specifically animated bounding boxes, allows for efficient KNN queries by dynamically adjusting the search radius and supporting arbitrary primitive types, thereby improving performance and reducing computational overhead.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If hardware-accelerated light transport simulation techniques are used, then query speed is improved, but device complexity increases
Solution Approach 1:
The patent introduces animated bounding boxes as an intermediary data structure that mediates between the query point and the primitive set. These bounding boxes dynamically expand to enclose primitives within the search radius, allowing the hardware accelerator to efficiently cull non-candidate primitives before performing detailed distance calculations, thus enabling fast queries without requiring complex custom hardware for each query type
Solution Approach 2:
The bounding boxes are made dynamic by animating their expansion over time or iteration. Instead of using fixed static bounding boxes, the system dynamically adjusts the bounding box size to match the search radius, allowing the same hardware infrastructure to handle varying query parameters efficiently without reconfiguration
2Adaptability or versatility
If animated bounding boxes are used to support arbitrary primitive types, then adaptability is improved, but computational overhead increases
Solution Approach 1:
The animated bounding box mechanism serves as a universal intermediary that works with multiple primitive types (points, triangles, lines, curves, polygons) through a common interface. The same bounding box expansion logic and hardware acceleration pipeline handle all primitive types, eliminating the need for type-specific query code paths and reducing overall computational overhead through consolidation
3Measurement precision
If unbounded search radii are supported, then measurement precision is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing bounding boxes for all primitives in the scene before query execution. During query processing, these pre-computed bounding boxes are animated to expand to the required search radius, allowing the hardware accelerator to quickly identify candidate primitives without performing expensive full-scene searches, thus maintaining precision even for unbounded radii
Data Source
AI summary
Apparatuses, systems, and techniques to perform a K-nearest-neighbor query. In at least one embodiment, a set of bounding boxes corresponding to a set of primitives is generated that allows the query to be solved using light transport simulation acceleration features of a GPU.