Matrix Transpose via Load Buffer Rotation
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Solution Overview
Problem
The computational expense and resource-intensive nature of matrix transpose operations in computer processor architectures, which can lead to significant hardware costs and performance slowdowns, especially in applications like deep learning and linear algebra.
Innovation Solution
A MATRIXTRANSPOSE instruction that utilizes existing processor hardware, such as a load rotator and shuffle network, to efficiently transpose data by rotating source rows into destination columns, thereby reducing the need for additional hardware and minimizing die area, and allowing for flexible and expandable matrix sizes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional matrix transpose operations are performed using conventional processor architectures, then the transpose operation can be completed, but the computational expense and hardware resource requirements increase significantly
Solution Approach 1:
The patent makes existing processor components (load buffer, shuffle network, rotator) perform matrix transpose operations in addition to their original functions. The load buffer that normally stores load data is repurposed to store matrix rows during transpose, the shuffle network that normally shuffles data is used to transpose elements, and the rotator that normally rotates data is used to rotate rows into columns, thereby reducing the need for dedicated transpose hardware
Solution Approach 2:
The processor uses its own existing hardware resources to perform the matrix transpose operation without requiring external or dedicated transpose hardware. The load buffer, shuffle network, and rotator serve the dual purpose of their original function and matrix transpose, making the system self-sufficient for transpose operations
2Speed
If dedicated hardware is added to accelerate matrix transpose operations, then the operation speed improves, but the die area and hardware costs increase
Solution Approach 1:
Existing processor components are made multi-functional to perform both their original operations and matrix transpose operations. The load buffer, shuffle network, and rotator are used for both data processing and matrix transpose, eliminating the need for dedicated transpose hardware and the associated die area
Solution Approach 2:
The matrix transpose functionality is merged with existing processor operations. The transpose operation is combined with the load, shuffle, and rotate operations that already exist in the processor pipeline, allowing both functions to share the same hardware resources
3Reliability
If matrix transpose is performed synchronously with the main processor pipeline, then data consistency is maintained, but the overall processing throughput decreases
Solution Approach 1:
The matrix transpose operation is segmented into discrete steps (loading rows into load buffer, shuffling elements, rotating rows to columns, writing to destination) that can be executed independently. This segmentation allows the transpose to be performed as a separate operation that doesn't block the main processor pipeline
Solution Approach 2:
The load buffer acts as an intermediary structure that temporarily holds matrix data during the transpose operation. By using the load buffer as a mediator between the source and destination matrices, the transpose operation can proceed independently without blocking the main processor pipeline
Data Source
AI summary
Disclosed embodiments relate to a method and apparatus for efficient matrix transpose. In one example, a processor to execute a matrix transpose instruction includes fetch circuitry to fetch the matrix transpose instruction specifying a destination matrix and a source matrix having (N×M) elements and (M×N) elements, respectively, a (N×M) load buffer, decode circuitry to decode the fetched matrix transpose instruction, and execution circuitry, responsive to the decoded matrix transpose instruction to, for each row X of M rows of the specified source matrix: fetch and buffer N elements of the row in a load register, and cause the N buffered elements to be written, in the same relative order as in the row, to column X of M columns of the load buffer, and the execution circuitry subsequently to write each of N rows of the load buffer to a same row of the load buffer.