Dual Pipeline Systolic Array for Matrix Throughput
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Solution Overview
Problem
Current parallel graphics data processing systems face challenges in efficiently performing high-throughput matrix operations, which are crucial for general-purpose numerical computing applications like deep learning and digital signal processing.
Innovation Solution
The implementation of a dual pipeline parallel systolic array architecture within graphics processing units (GPUs), which enables efficient execution of matrix multiplication and add operations by utilizing two separate pipelines for data processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional fixed function computational units are used for graphics processing, then graphics data processing can be performed, but high-throughput matrix operations for general-purpose numerical computing cannot be efficiently executed
Solution Approach 1:
The patent implements a dual pipeline parallel systolic array architecture that can perform both traditional graphics processing operations and general-purpose numerical computing matrix operations. The processing elements are designed with universal functionality to execute different types of computations (graphics-specific operations and matrix multiply-add operations) depending on the workload requirements, thereby achieving multi-functionality that resolves the contradiction between adaptability and productivity.
Solution Approach 2:
The patent divides the computational architecture into two separate pipelines: a first pipeline for graphics data processing operations and a second pipeline for matrix operations. This segmentation allows each pipeline to be optimized for its specific workload, with the matrix pipeline incorporating specialized systolic array structures for high-throughput matrix multiply-add operations while the graphics pipeline handles traditional rendering tasks, thus improving overall adaptability and productivity.
2Productivity
If a single pipeline architecture is used for data processing, then device complexity is reduced, but processing throughput for matrix operations is limited
Solution Approach 1:
The patent segments the data processing function into two independent pipelines: a first pipeline for graphics data processing and a second pipeline for matrix operations. Each pipeline operates independently with its own processing elements, data paths, and control logic. This segmentation enables the matrix pipeline to achieve high throughput through specialized systolic array configurations while keeping each individual pipeline's complexity manageable through modular design.
Solution Approach 2:
The patent introduces a dimensional change by adding a second pipeline dimension alongside the existing first pipeline. Instead of increasing throughput within a single pipeline by adding more stages (which would increase complexity linearly), the system adds a parallel dimension with a second pipeline that handles matrix operations separately. This dimensional approach enables throughput scaling without proportionally increasing the complexity of any single pipeline.
3Productivity
If processing elements are designed for specific graphics operations, then graphics processing performance is optimized, but flexibility for matrix multiplication operations is reduced
Solution Approach 1:
The patent segments the processing architecture into two distinct pipelines with specialized processing elements: the first pipeline contains processing elements optimized for graphics data processing operations, while the second pipeline contains processing elements configured for matrix multiplication operations. Each pipeline's processing elements are tailored to their specific function, with the matrix pipeline incorporating systolic array configurations that are inherently suited for high-performance matrix operations, thereby achieving both optimized graphics performance and flexible matrix computation capability.
Solution Approach 2:
The dual pipeline architecture provides universality by allowing the system to selectively activate either the first pipeline for graphics operations or the second pipeline for matrix operations, or both simultaneously. The processing elements in each pipeline are designed with sufficient functionality to handle their respective workloads efficiently, with the matrix pipeline's systolic array configuration providing universal support for various matrix operation types (multiply, add, dot product) while maintaining optimized performance characteristics.
Data Source
AI summary
A processing apparatus described herein includes a general-purpose parallel processing engine comprising a systolic array having multiple pipelines, each of the multiple pipelines including multiple pipeline stages, wherein the multiple pipelines include a first pipeline, a second pipeline, and a common input shared between the first pipeline and the second pipeline.