GPU Compute Mechanism for Matrix-Vector Transformations
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Solution Overview
Problem
Current parallel graphics data processing systems face limitations in efficiently processing graphics data due to fixed function computational units and the need for increased parallel processing techniques.
Innovation Solution
The implementation of a graphics processing unit (GPU) with a compute mechanism that includes multiple processing units, each comprising different types of execution units, optimized for matrix-vector transformations using a register file or shared local memory.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If fixed function computational units are used in graphics processors, then specific graphics operations can be processed efficiently, but the processor cannot support a wider variety of operations and lacks flexibility
Solution Approach 1:
The patent applies universality by replacing fixed function units with general-purpose processing units that can execute multiple types of operations including graphics processing, matrix operations, and other compute tasks. The processing units are designed with general computational capabilities that can be configured through software to handle different workloads, making the graphics processor versatile while maintaining performance.
2Productivity
If parallel processing techniques are implemented to increase processing capacity, then more graphics data can be processed simultaneously, but the complexity of the processing system increases
Solution Approach 1:
The patent applies segmentation by dividing the processing system into multiple independent processing units that can operate in parallel. Each processing unit is a self-contained module with execution units, register files, and memory access capabilities, allowing the system to handle large datasets by distributing work across multiple units while maintaining manageable complexity through modular architecture.
Solution Approach 2:
The patent merges multiple processing units into a unified architecture where units share common resources such as memory controllers and interconnect structures. This combining approach enables parallel processing capacity while reducing overall system complexity by eliminating redundant components and establishing efficient resource sharing mechanisms across the parallel units.
3Adaptability or versatility
If multiple types of execution units are implemented in processing units, then flexibility and adaptability improve, but the device complexity increases
Solution Approach 1:
The patent applies local quality by implementing different types of execution units (such as matrix execution units, vector execution units, and scalar execution units) within processing units based on the specific computational requirements of different operations. Each execution unit type is optimized for its intended function, allowing the system to achieve flexibility and adaptability by selecting appropriate units for specific tasks while managing complexity through targeted specialization rather than uniform design.
Data Source
AI summary
Embodiments provide mechanisms to facilitate compute operations for deep neural networks. One embodiment comprises a graphics processing unit comprising one or more multiprocessors, at least one of the one or more multiprocessors including a register file to store a plurality of different types of operands and a plurality of processing cores. The plurality of processing cores includes a first set of processing cores of a first type and a second set of processing cores of a second type. The first set of processing cores are associated with a first memory channel and the second set of processing cores are associated with a second memory channel.