Texture Filtering Logic Units for Deep Learning
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Solution Overview
Problem
Current GPUs leave texture filtering logic units idle during deep learning operations, as they utilize separate shader logic units or dedicated logic cores, resulting in underutilization of system resources.
Innovation Solution
Configuring texture filtering logic units in the graphics processing pipeline to perform deep learning operations by mapping inputs to texture filtering logic units, executing convolution and matrix multiply operations, and communicating outputs to programmable shaders.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If separate shader logic units or dedicated logic cores are used to perform deep learning operations, then deep learning operations can be executed, but texture filtering logic units remain idle resulting in underutilization of system resources
Solution Approach 1:
The texture filtering logic units are reconfigured to perform dual functions: traditional texture filtering for graphics rendering and deep learning operations such as convolution and matrix multiplication. This multi-functionality allows the same hardware units to serve both graphics processing and AI workloads, eliminating resource idle time without requiring separate dedicated logic cores
2Productivity
If texture filtering logic units are reconfigured for deep learning operations, then system resource utilization is maximized, but the original graphics processing functionality must be temporarily suspended
Solution Approach 1:
The GPU alternates between executing graphics processing tasks and deep learning operations by periodically switching the configuration of texture filtering logic units. During graphics rendering, these units perform texture filtering; during AI workloads, they are reconfigured for convolution and matrix multiplication. This periodic switching allows both functions to be supported over time without permanent suspension of either
Solution Approach 2:
The system dynamically reconfigures the texture filtering logic units based on the current workload type. Configuration switching is performed on-the-fly depending on whether graphics or deep learning operations are being executed, allowing the hardware to adapt its functionality in real-time without requiring separate fixed-function units for each operation type
Data Source
AI summary
The present disclosure relates to methods and apparatus for configuring a texture filtering logic unit for deep learning operation. The apparatus can map one or more inputs of a deep learning operation to a respective input of a texture filtering logic unit in a graphics pipeline. Moreover, the apparatus can generate, by the texture filtering logic unit, at least one output for the deep learning operation based on the one or more inputs mapped to the texture filtering logic unit. Furthermore, the apparatus can communicate the at least one output to a programmable shader, which can analyze the output result to determine information relating to an input image based on the deep learning operation.