Systolic Array Matrix Accelerator for Versatile Deep Learning
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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 matrix accelerator using a systolic array architecture within a parallel or graphics processing unit, allowing for efficient execution of matrix multiplication and add operations, as well as dot product operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed function computational unit is used for graphics data processing, then the processing of specific graphics operations is efficient, but the ability to support a wider variety of operations is limited
Solution Approach 1:
The patent implements a universal matrix accelerator that can perform multiple types of operations including matrix multiplication, matrix addition, and dot product operations. This single computational unit replaces the need for separate fixed-function units for different operations, thereby increasing operational versatility while managing system complexity through consolidation
2Productivity
If programmable graphics processors are used for general purpose numerical computing, then the ability to perform various computing applications is improved, but the processing throughput for matrix operations is insufficient
Solution Approach 1:
The patent segments the matrix accelerator into multiple independent processing elements that can operate in parallel. Each processing element handles specific computational tasks, and their coordinated operation achieves high throughput for matrix operations. This segmented architecture maintains manageability while significantly improving productivity compared to general-purpose programmable processors
3Speed
If traditional graphics processing units are used for deep learning and digital signal processing, then the processing speed of matrix operations is limited, but the system architecture remains relatively simple
Solution Approach 1:
The patent replaces traditional sequential graphics processing mechanisms with a specialized matrix accelerator that uses parallel processing elements and optimized data flow paths. This substitution of the underlying processing mechanism enables significantly faster matrix processing speeds required for deep learning and digital signal processing applications
Data Source
AI summary
A processing apparatus includes a processing resource including a general-purpose parallel processing engine and a matrix accelerator. The matrix accelerator includes first circuitry to receive a command to perform operations associated with an instruction, second circuitry to configure the matrix accelerator according to a physical depth of a systolic array within the matrix accelerator and a logical depth associated with the instruction, third circuitry to read operands for the instruction from a register file associated with the systolic array, fourth circuitry to perform operations for the instruction via one or more passes through one or more physical pipeline stages of the systolic array based on a configuration performed by the second circuitry, and fifth circuitry to write output of the operations to the register file associated with the systolic array.