Matrix Transpose-Multiply Instructions for Tile-Based Processor Throughput
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Solution Overview
Problem
Existing processors face difficulties in efficiently handling large matrices due to instruction intensity and inefficient matrix operations, particularly in tasks like deep learning where low precision arithmetic is common.
Innovation Solution
Implementing matrix operations using 2-dimensional data structures called tiles, which are processed through specialized hardware and instructions such as matrix (tile) multiplication, accumulation, and transposition, supported by tile configuration and shared memory systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional processors handle large matrices using standard instructions, then general-purpose computing is maintained, but processing throughput and energy efficiency deteriorate
Solution Approach 1:
The patent divides large matrices into smaller 2D tile structures that can be processed efficiently. By segmenting the matrix data into tiles with specific row and column dimensions, the processor can handle each tile independently using optimized hardware circuits, thereby improving throughput and energy efficiency for large-scale matrix operations.
Solution Approach 2:
The patent introduces specialized matrix operation circuits as intermediary hardware between the processor core and memory system. These dedicated circuits act as mediators that perform matrix-specific operations (multiplication, accumulation, transposition) more efficiently than general-purpose instructions, resolving the contradiction between maintaining general computing and achieving high performance for specific workloads.
2Power
If specialized hardware for matrix multiplication is implemented, then peak compute and energy efficiency improve, but device complexity increases
Solution Approach 1:
The patent designs matrix operation circuits that can perform multiple functions (matrix multiplication, matrix accumulation, matrix transposition) using a unified hardware architecture. This multi-functionality allows the specialized hardware to achieve high peak compute capability while avoiding the complexity of implementing separate dedicated circuits for each operation type.
Solution Approach 2:
The patent transitions from traditional 1D vector processing to 2D tile-based matrix processing. By organizing data and operations in two dimensions with configurable row and column parameters, the hardware can process multiple data elements simultaneously in a grid structure, achieving higher compute density without proportionally increasing complexity.
3Productivity
If low precision arithmetic is used in deep learning, then throughput increases, but measurement precision may be compromised
Solution Approach 1:
The patent enables dynamic adjustment of precision parameters in the matrix operation circuits. By allowing configuration of data element width and accumulation precision, the system can optimize for throughput using lower precision when appropriate, while maintaining the capability to use higher precision when accuracy requirements demand, thus resolving the contradiction between speed and precision.
Data Source
AI summary
Embodiments for a matrix transpose and multiply operation are disclosed. In an embodiment, a processor includes a decoder and execution circuitry. The decoder is to decode an instruction having a format including an opcode field to specify an opcode, a first destination operand field to specify a destination matrix location, a first source operand field to specify a first source matrix location, and a second source operand field to specify a second source matrix location. The execution circuitry is to, in response to the decoded instruction, transpose the first source matrix to generate a transposed first source matrix, perform a matrix multiplication using the transposed first source matrix and the second source matrix to generate a result, and store the result in a destination matrix location.